CN103488089B - Adaptive pesticide waste liquid incinerator hazardous emission controls up to par system and method - Google Patents

Abstract

The invention discloses a kind of adaptive pesticide waste liquid incinerator hazardous emission controls up to par system and method.It comprises incinerator, intelligent instrument, DCS system, data-interface and host computer; DCS system comprises control station and database; Be connected with DCS system for measuring the intelligent instrument easily surveying variable, DCS system is connected with host computer by data-interface.Host computer carries out standardization pre-service to training sample, fuzzy neural network is adopted to set up regression model, by introducing support vector machine, best optimizing is carried out to the linear dimensions in fuzzy neural network, solve the problem of fuzzy neural network optimum configurations, according to the change of training sample, self-adaptative adjustment is carried out to the structure of whole fuzzy neural network simultaneously; Host computer also has the function of model modification and result display.The present invention has the advantages such as whether on-line measurement COD, effectively monitoring COD exceed standard, control COD emission compliance, noise resisting ability are strong, on-line optimization parameter, self-adapted adjustment system structure.

Description

Self-adaptive pesticide waste liquid incinerator harmful substance emission standard control system and method

Technical Field

The invention relates to the field of pesticide production, in particular to a self-adaptive standard control system and method for harmful substance emission of a pesticide waste liquid incinerator.

Background

China is a big country for producing and using pesticides, the number of pesticide production enterprises reaches about 4100, more than 500 raw pesticide production enterprises exist, and statistical data of the Ministry of agriculture of China shows that the total yield of pesticides reaches 171.1 ten thousand tons in 2008 in 1-11 months. The irrational structure of the agricultural chemical varieties in China further increases the difficulty of environmental management. According to incomplete statistics, the amount of wastewater discharged by the pesticide industry all over the country is about 15 hundred million tons every year. Wherein, the treatment reaches the standard and only accounts for 1 percent of the treated treatment. The incineration method is the most effective and thorough method for treating pesticide residue and waste residue at present and is the most common method for application. The Chemical Oxygen Demand (COD) of waste water after incineration is the most important index for the discharge of harmful substances after the incineration of pesticide waste liquid, but the COD cannot be measured on line, and the off-line measurement needs four or five hours once, so that the working condition change cannot be reflected in time and the actual production cannot be guided. Therefore, COD is seriously out of limits in the actual incineration process.

Zadeh first proposed the concept of a Fuzzy set in 1965 by american mathematician l. Fuzzy logic then begins to replace the classical logic that persists in that everything can be represented in terms of binary terms in a way that it more closely resembles the question and semantic statement of everyday people. In 1987, Bart Kosko's rate first performed a more systematic study of the combination of fuzzy theory and neural networks. In the following time, the theory and application of the fuzzy neural network are developed rapidly, and the proposal of various new fuzzy neural network models and the research of the adaptive learning algorithm not only accelerate the perfection of the fuzzy neural network, but also are widely applied in practice. However, the determination of the fuzzy neural network structure also encounters the same problems as the neural network, and the structure parameters need to be determined manually by an operator depending on own operation experience.

The support vector machine, introduced by Vapnik in 1998, converts the original optimal classification problem into a dual optimization problem by using a structure risk minimization method in statistical theory learning instead of a general empirical structure minimization method, thereby having good popularization capability and being widely applied to pattern recognition, fitting and classification problems. In this scheme, a support vector machine is used to optimize parameters in the fuzzy neural network model.

Disclosure of Invention

In order to overcome the defects that COD can not be measured on line and COD seriously exceeds the standard in the conventional incinerator process, the invention provides a system and a method for controlling the emission of harmful substances of a pesticide waste liquid incinerator to reach the standard, which have the advantages of measuring COD on line, effectively monitoring whether the COD exceeds the standard, controlling the emission of the COD to reach the standard, having strong noise resistance, optimizing parameters on line, adaptively adjusting the system structure and the like.

The technical scheme adopted by the invention for solving the technical problems is as follows:

the self-adaptive pesticide waste liquid incinerator harmful substance emission standard control system comprises an incinerator, an intelligent instrument, a DCS (distributed control system), a data interface and an upper computer, wherein the DCS comprises a control station and a database; on-spot intelligent instrument and DCS headtotail, the DCS system is connected with the host computer, the host computer include:

the data preprocessing module is used for preprocessing the model training samples input from the DCS database, so that the mean value of the training samples is 0, and the variance of the training samples is 1, and the processing is completed by adopting the following formula:

calculating an average value: <math> <mrow> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance: <math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization: <math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>TX</mi> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>&sigma;</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein, TX_iThe ith training sample is the data of key variables, Chemical Oxygen Demand (COD) and corresponding operating variables for enabling COD to be discharged when the production is normal, which are collected from a DCS database, N is the number of the training samples,is the mean of the training samples, and X is the normalized training sample. Sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples. And the fuzzy neural network module is used for carrying out fuzzy reasoning and establishing a fuzzy rule on the input variable transmitted from the data preprocessing module. And carrying out fuzzy classification on the preprocessed training sample X transmitted from the data preprocessing module to obtain the center and the width of each fuzzy cluster in the fuzzy rule base. Let the p-th normalized training sample X_p=[X_p1,…,X_pn]Where n is the number of input variables.

Let the fuzzy neural network have R fuzzy rules, for obtaining each fuzzy rule for training sample X_pEach input variable X of_pjJ =1, …, n, whose membership to the i-th fuzzy rule is to be found by the following fuzzy equation:

<math> <mrow> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein m is_ijAnd σ_ijAnd respectively representing the center and the width of the jth Gaussian member function of the ith fuzzy rule, and obtaining the center and the width by fuzzy clustering.

Let training sample X_pFitness to fuzzy rule i is mu⁽ⁱ⁾(X_p) Then μ⁽ⁱ⁾(X_p) Can be determined by the following formula:

<math> <mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&Pi;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

after the fitness of the input training sample to each rule is obtained, the fuzzy neural network deduces the output of the fuzzy rule to obtain the final analytic solution. In a commonly used fuzzy neural network structure, the process of deriving each fuzzy rule can be expressed as follows: firstly, the linear product sum of all input variables in the training sample is obtained, and then the linear product sum is used to match the fitness mu of the rule⁽ⁱ⁾(X_p) And multiplying to obtain the final output of each fuzzy rule. The derived output of the fuzzy rule i can be expressed as follows:

<math> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula (f)⁽ⁱ⁾For the output of the ith fuzzy rule,is the predicted output of the fuzzy neural network model to the p-th training sample, a_ijJ =1, …, n is the linear coefficient of the jth variable in the ith fuzzy rule, a_i0Is a constant term of the linear product sum of the input variables in the ith fuzzy rule, and b is an output offset.

In formula (7), the determination of parameters in the input variable linear product sum is a main problem used in the use of the fuzzy neural network, here, the original fuzzy rule derivation output form is converted into the support vector machine optimization problem, and then the support vector machine is used for linear optimization, wherein the conversion process is as follows:

<math> <mrow> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <mi>b</mi> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein X_p0Is a constant term and is constant equal to 1. Order to

<math> <mrow> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>]</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

Wherein,the conversion form of the original training sample is represented, namely, the original training sample is converted into the form of the formula as above, and the form is used as the training sample of the support vector machine:

<math> <mrow> <mi>S</mi> <mo>=</mo> <mo>{</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein y is₁,…,y_NThe target output of the training sample is taken, S is taken as a new input training sample set, and then the original problem can be converted into the following dual optimization problem of the support vector machine:

<math> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&gamma;</mi> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msub> <mi>L</mi> <mi>&epsiv;</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>p</mi> </msub> <mo>,</mo> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mi>&omega;</mi> <mi>T</mi> </msup> <mi>&omega;</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow> </math>

y_pis inputting a training sample X_pTarget output of f (X)_p) Is corresponding to X_pModel output of, L (y_p,f(X_p) Is an input training sample X_pCorresponding target output y_pAnd the model output f (X)_p) The first order insensitive function when the error margin of the optimization problem is. Gamma is the penalty factor of the support vector machine, R (omega, b) is the objective function of the optimization problem, N is the number of training samples, L (y_p,f(X_p) Expression) as follows:

wherein, the error tolerance of the optimization problem is obtained, then a support vector machine is used for obtaining the optimal derivation linear parameter of the fuzzy rule of the fuzzy neural network and the forecast output of the dual optimization problem:

<math> <mrow> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>&Element;</mo> <mi>SV</mi> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>R</mi> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>&lt;</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>,</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>></mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein,are each y_p-f(X_p) Lagrange multipliers corresponding to greater than 0 and less than 0,the predicted COD value corresponding to the p-th training sample and the operation variable value for making the COD discharge reach the standard are obtained.I.e. corresponding to the p-th training sample X_pThe operating variable value at which chemical oxygen demand is lowest.

The adaptive structure optimization module is mainly determined by artificial experience in the determination of the structural parameters of the fuzzy neural network, and once the structural parameters are determined, the whole model structure cannot be adaptively optimized. The module increases the threshold value mu by setting a fuzzy rule_th-addFuzzy rule importance reduction threshold mu_th-dFuzzy rule pruning threshold mu_th-delAnd carrying out self-adaptive adjustment on the structure of the fuzzy neural network in the process of processing the training sample. In equation (5), the fuzzy rule i is applied to the p-th training sample X_p=[X_p1,…,X_pn]Has a fitness of mu⁽ⁱ⁾(X_p) And the fuzzy rule item with the maximum fitness value in the fuzzy rules is as follows:

<math> <mrow> <mi>I</mi> <mo>=</mo> <mi>arg</mi> <munder> <mi>max</mi> <mrow> <mn>1</mn> <mo>&le;</mo> <mi>i</mi> <mo>&le;</mo> <mi>R</mi> </mrow> </munder> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinItem numbers representing fuzzy rule items of greatest fitness value, i.e.

If μ^(I)<μ_th-addThat is, the maximum value of the adaptability of the fuzzy rule is less than the set fuzzy rule increasing threshold value mu_th-addThen a new rule is added. The center and width of the gaussian membership function of the newly added fuzzy rule are:

$m_j^{\mathrm{new}}=X_{\mathrm{pj}},j=1,...,n---\left(16\right)$

<math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>j</mi> <mi>new</mi> </msubsup> <mo>=</mo> <mi>&beta;</mi> <mo>&times;</mo> <mfrac> <msup> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>Ij</mi> </msub> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>Ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAndconstant beta for the center and width of the Gaussian membership function of the new fuzzy rule>0 denotes the degree of overlap between the new fuzzy rule and the fuzzy rule I, and the value of β typically takes 1.2.

In order to prevent the number of fuzzy rules from increasing, an adaptive method is adopted to determine whether the fuzzy rules are deleted or not: if the ith fuzzy rule is applied to the p training sample⁽ⁱ⁾(X_p) Less than the fuzzy rule significance reduction threshold mu_th-dThe fuzzy rule importance is decreased and vice versa, wherein the importance of the ith fuzzy rule is D_iTo represent; if the ith rule is fuzzy D_iThe value is reduced to the fuzzy rule reduction threshold mu in the training process of the training sample_th-delThen the ith fuzzy rule is deleted.

Importance value D for each fuzzy rule_iI =1, …, and the initial value of R is set to 1, which varies with the input training sample as shown in equation (18):

wherein D_iThe importance of the ith fuzzy rule is shown, and the constant tau value determines the speed of the change of the importance of the fuzzy rule, and is 1, mu⁽ⁱ⁾(X_p) Denotes the adaptation value, μ, of the ith fuzzy rule to the p-th training sample_th-dIndicating a fuzzy rule significance reduction threshold.

When D is present_iSatisfies D_i<μ_th-delWhere μ_th-delIf 0.005 is taken, the ith fuzzy rule is deleted.

As a preferred solution: the host computer still include: and the discrimination model updating module is used for acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured chemical oxygen consumption with a function forecast value, and if the relative error is more than 10% or the actually-measured COD data does not reach the standard, adding new data which reaches the standard when the data is normally produced in the DCS database into the training sample data to update the model.

Further, the host computer still include: the result display module is used for transmitting the COD forecast value and the operation variable value which enables the COD emission to reach the standard to the DCS, displaying the process state at a control station of the DCS, and transmitting the process state information to a field operation station for displaying through the DCS and a field bus; meanwhile, the DCS system takes the obtained operation variable value which enables the COD emission to reach the standard as a new operation variable set value, and automatically executes the COD emission standard-reaching operation.

And the signal acquisition module is used for acquiring data from the database according to the set time interval of each sampling.

Still further, the key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator, and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.

The control method for the standard reaching of the harmful substance emission is realized by the adaptive standard reaching control system for the harmful substance emission of the pesticide waste liquid incinerator, and the control method is concretely realized by the following steps: :

1) determining key variables used by a harmful substance emission process object of the pesticide waste liquid incinerator according to process analysis and operation analysis, acquiring data of the variables during normal production from a DCS (distributed control system) database as an input matrix of a training sample TX, and acquiring corresponding COD (chemical oxygen demand) and operation variable data for enabling the COD emission to reach the standard as an output matrix Y;

2) the method is used for preprocessing the model training samples input from the DCS database, so that the mean value of the training samples is 0, the variance is 1, and the processing is completed by adopting the following formula process:

calculating an average value: <math> <mrow> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance: <math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization: <math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>TX</mi> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>&sigma;</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein, TX_iThe ith training sample is the data of key variables, Chemical Oxygen Demand (COD) and corresponding operating variables for enabling COD to be discharged when the production is normal, which are collected from a DCS database, N is the number of the training samples,is the mean of the training samples, and X is the normalized training sample. Sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples.

3) And carrying out fuzzy reasoning and establishing a fuzzy rule on the input variable transmitted from the data preprocessing module. And carrying out fuzzy classification on the preprocessed training sample X transmitted from the data preprocessing module to obtain the center and the width of each fuzzy cluster in the fuzzy rule base. Let the p-th normalized training sample X_p=[X_p1,…,X_pn]Where n is the number of input variables.

Let the fuzzy neural network have R fuzzy rules, for obtaining each fuzzy rule for training sample X_pEach input variable X of_pjJ =1, …, n, whose membership to the i-th fuzzy rule is to be found by the following fuzzy equation:

<math> <mrow> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein m is_ijAnd σ_ijAnd respectively representing the center and the width of the jth Gaussian member function of the ith fuzzy rule, and obtaining the center and the width by fuzzy clustering.

Let training sample X_pFitness to fuzzy rule i is mu⁽ⁱ⁾(X_p) Then μ⁽ⁱ⁾(X_p) Can be determined by the following formula:

<math> <mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&Pi;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

after the fitness of the input training sample to each rule is obtained, the fuzzy neural network deduces the output of the fuzzy rule to obtain the final analytic solution. In a commonly used fuzzy neural network structure, the process of deriving each fuzzy rule can be expressed as follows: firstly, the linear product sum of all input variables in the training sample is obtained, and then the linear product sum is used to match the fitness mu of the rule⁽ⁱ⁾(X_p) And multiplying to obtain the final output of each fuzzy rule. The derived output of the fuzzy rule i can be expressed as follows:

<math> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula (f)⁽ⁱ⁾For the output of the ith fuzzy rule,is the predicted output of the fuzzy neural network model to the p-th training sample, a_ijJ =1, …, n is the linear coefficient of the jth variable in the ith fuzzy rule, a_i0Is a constant term of the linear product sum of the input variables in the ith fuzzy rule, and b is an output offset.

4) In the formula (7), the determination of the parameters in the linear product sum of the input variables is a main problem used in the use of the fuzzy neural network, here, the original fuzzy rule derivation output form is converted into the optimization problem of the support vector machine, and then the linear optimization is performed by using the support vector machine, wherein the conversion process is as follows:

<math> <mrow> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <mi>b</mi> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein X_p0Is a constant term and is constant equal to 1. Order to

<math> <mrow> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>]</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

Wherein,the conversion form of the original training sample is represented, namely, the original training sample is converted into the form of the formula as above, and the form is used as the training sample of the support vector machine:

<math> <mrow> <mi>S</mi> <mo>=</mo> <mo>{</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein y is₁,…,y_NThe target output of the training sample is taken, S is taken as a new input training sample set, and then the original problem can be converted into the following dual optimization problem of the support vector machine:

<math> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&gamma;</mi> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msub> <mi>L</mi> <mi>&epsiv;</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>p</mi> </msub> <mo>,</mo> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mi>&omega;</mi> <mi>T</mi> </msup> <mi>&omega;</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein y is_pIs inputting a training sample X_pTarget output of f (X)_p) Is corresponding to X_pModel output of, L (y_p,f(X_p) Is an input training sample X_pCorresponding target output y_pAnd the model output f (X)_p) The first order insensitive function when the error margin of the optimization problem is. Gamma is the penalty factor of the support vector machine, R (omega, b) is the objective function of the optimization problem, N is the number of training samples, L (y_p,f(X_p) Watch (C)The expression is as follows:

wherein, the error tolerance of the optimization problem is obtained, then a support vector machine is used for obtaining the optimal derivation linear parameter of the fuzzy rule of the fuzzy neural network and the forecast output of the dual optimization problem:

<math> <mrow> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>&Element;</mo> <mi>SV</mi> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>R</mi> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>&lt;</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>,</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>></mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAre each y_p-f(X_p) Lagrange multipliers corresponding to greater than 0 and less than 0,the predicted COD value corresponding to the p-th training sample and the operation variable value for making the COD discharge reach the standard are obtained.

5) Is provided withFuzzy rule-based increase threshold mu_th-addFuzzy rule importance reduction threshold mu_th-dFuzzy rule pruning threshold mu_th-delAnd carrying out self-adaptive adjustment on the structure of the fuzzy neural network in the process of processing the training sample. In equation (5), the fuzzy rule i is applied to the p-th training sample X_p=[x₁,…,x_n]Has a fitness of mu⁽ⁱ⁾(X_p) And the fuzzy rule item with the maximum fitness value in the fuzzy rules is as follows:

<math> <mrow> <mi>I</mi> <mo>=</mo> <mi>arg</mi> <munder> <mi>max</mi> <mrow> <mn>1</mn> <mo>&le;</mo> <mi>i</mi> <mo>&le;</mo> <mi>R</mi> </mrow> </munder> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinItem numbers representing fuzzy rule items of greatest fitness value, i.e.

If μ^(I)<μ_th-addThat is, the maximum value of the adaptability of the fuzzy rule is less than the set fuzzy rule increasing threshold value mu_th-addThen a new rule is added. The center and width of the gaussian membership function of the newly added fuzzy rule are:

$m_j^{\mathrm{new}}=X_{\mathrm{pj}},j=1,...,n---\left(16\right)$

<math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>j</mi> <mi>new</mi> </msubsup> <mo>=</mo> <mi>&beta;</mi> <mo>&times;</mo> <mfrac> <msup> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>Ij</mi> </msub> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>Ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAndconstant beta for the center and width of the Gaussian membership function of the new fuzzy rule>0 denotes the degree of overlap between the new fuzzy rule and the fuzzy rule I, and the value of β typically takes 1.2.

In order to prevent the increase of the number of fuzzy rules, an adaptive method is adoptedDetermining whether the fuzzy rule is deleted or not: if the ith fuzzy rule is applied to the p training sample⁽ⁱ⁾(X_p) Less than the fuzzy rule significance reduction threshold mu_th-dThe fuzzy rule importance is decreased and vice versa, wherein the importance of the ith fuzzy rule is D_iTo represent; if the ith rule is fuzzy D_iThe value is reduced to the fuzzy rule reduction threshold mu in the training process of the training sample_th-delThen the ith fuzzy rule is deleted.

Importance value D for each fuzzy rule_iI =1, …, and the initial value of R is set to 1, which varies with the input training sample as shown in equation (18):

wherein D_iThe importance of the ith fuzzy rule is shown, and the constant tau value determines the speed of the change of the importance of the fuzzy rule, and is 1, mu⁽ⁱ⁾(X_p) Denotes the adaptation value, μ, of the ith fuzzy rule to the p-th training sample_th-dIndicating a fuzzy rule significance reduction threshold.

When D is present_iSatisfies D_i<μ_th-delWhere μ_th-delIf 0.005 is taken, the ith fuzzy rule is deleted.

As a preferred solution: the method further comprises the following steps: 6) and acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured chemical oxygen demand with a function forecast value, and if the relative error is more than 10% or the actually-measured COD data does not reach the standard, adding new data which reaches the standard when the data is normally produced in the DCS database into training sample data, and updating the model.

Further, in the step 4), transmitting the COD forecast value and the operation variable value which enables the COD emission to reach the standard to the DCS, displaying the process state at a control station of the DCS, and transmitting the process state information to a field operation station for displaying through the DCS and a field bus; meanwhile, the DCS system takes the obtained operation variable value which enables the COD emission to reach the standard as a new operation variable set value, and automatically executes the COD emission standard-reaching operation.

Still further, the key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator, and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.

The technical conception of the invention is as follows: the invention provides a self-adaptive pesticide waste liquid incinerator harmful substance emission standard control system and method, and aims to find out an operation variable value for enabling chemical oxygen demand emission to reach a standard.

The invention has the following beneficial effects: 1. establishing an online soft measurement model of quantitative relation between system key variables and chemical oxygen demand emission; 2. quickly finding out the operation condition for reaching the discharge of the chemical oxygen demand.

Drawings

FIG. 1 is a hardware block diagram of the system proposed by the present invention;

fig. 2 is a functional structure diagram of the upper computer according to the present invention.

Detailed Description

The invention is further described below with reference to the accompanying drawings. The examples are intended to illustrate the invention, but not to limit the invention, and any modifications and variations of the invention within the spirit and scope of the claims are intended to fall within the scope of the invention.

Example 1

Referring to fig. 1 and 2, the adaptive pesticide waste liquid incinerator harmful substance emission standard control system comprises a field intelligent instrument 2 connected with an incinerator 1, a DCS system and an upper computer 6, wherein the DCS system comprises a data interface 3, a control station 5 and a database 4, the field intelligent instrument 2 is connected with the data interface 3, the data interface is connected with the control station 5, the database 4 and the upper computer 6, and the upper computer 6 comprises:

the data preprocessing module is used for preprocessing the model training samples input from the DCS database, so that the mean value of the training samples is 0, and the variance of the training samples is 1, and the processing is completed by adopting the following formula:

calculating an average value: <math> <mrow> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance: <math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization: <math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>TX</mi> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>&sigma;</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein, TX_iThe ith training sample is the data of key variables, Chemical Oxygen Demand (COD) and corresponding operating variables for enabling COD to be discharged when the production is normal, which are collected from a DCS database, N is the number of the training samples,is the mean of the training samples, and X is the normalized training sample. Sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples. And the fuzzy neural network module is used for carrying out fuzzy reasoning and establishing a fuzzy rule on the input variable transmitted from the data preprocessing module. And carrying out fuzzy classification on the preprocessed training sample X transmitted from the data preprocessing module to obtain the center and the width of each fuzzy cluster in the fuzzy rule base. Let the p-th normalized training sample X_p=[X_p1,…,X_pn]Where n is the number of input variables.

Let the fuzzy neural network have R fuzzy rules, for obtaining each fuzzy rule for training sample X_pEach input variable X of_pjJ =1, …, n, whose membership to the i-th fuzzy rule is to be found by the following fuzzy equation:

<math> <mrow> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein m is_ijAnd σ_ijAnd respectively representing the center and the width of the jth Gaussian member function of the ith fuzzy rule, and obtaining the center and the width by fuzzy clustering.

Let training sample X_pFitness to fuzzy rule i is mu⁽ⁱ⁾(X_p) Then μ⁽ⁱ⁾(X_p) Can be determined by the following formula:

<math> <mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&Pi;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

after the fitness of the input training sample to each rule is obtained, the fuzzy neural network deduces the output of the fuzzy rule to obtain the final analytic solution. In a commonly used fuzzy neural network structure, the process of deriving each fuzzy rule can be expressed as follows: firstly, the linear product sum of all input variables in the training sample is obtained, and then the linear product sum is used to match the fitness mu of the rule⁽ⁱ⁾(X_p) And multiplying to obtain the final output of each fuzzy rule. The derived output of the fuzzy rule i can be expressed as follows:

<math> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula (f)⁽ⁱ⁾For the output of the ith fuzzy rule,is the predicted output of the fuzzy neural network model to the p-th training sample, a_ijJ =1, …, n is the linear coefficient of the jth variable in the ith fuzzy rule, a_i0Is a constant term of the linear product sum of the input variables in the ith fuzzy rule, and b is an output offset.

In formula (7), the determination of parameters in the input variable linear product sum is a main problem used in the use of the fuzzy neural network, here, the original fuzzy rule derivation output form is converted into the support vector machine optimization problem, and then the support vector machine is used for linear optimization, wherein the conversion process is as follows:

<math> <mrow> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <mi>b</mi> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein X_p0Is a constant term and is constant equal to 1. Order to

<math> <mrow> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>]</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

Wherein,the conversion form of the original training sample is represented, namely, the original training sample is converted into the form of the formula as above, and the form is used as the training sample of the support vector machine:

<math> <mrow> <mi>S</mi> <mo>=</mo> <mo>{</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mrow> <mo>(</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>y</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein y is₁,…,y_NThe target output of the training sample is taken, S is taken as a new input training sample set, and then the original problem can be converted into the following dual optimization problem of the support vector machine:

<math> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&gamma;</mi> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msub> <mi>L</mi> <mi>&epsiv;</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>p</mi> </msub> <mo>,</mo> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mi>&omega;</mi> <mi>T</mi> </msup> <mi>&omega;</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow> </math>

y_pis inputting a training sample X_pTarget output of f (X)_p) Is corresponding to X_pModel output of, L (y_p,f(X_p) Is an input training sample X_pCorresponding target output y_pAnd the model output f (X)_p) Margin of error in optimization problemIs a first order insensitive function of time. Gamma is the penalty factor of the support vector machine, R (omega, b) is the objective function of the optimization problem, N is the number of training samples, L (y_p,f(X_p) Expression) as follows:

wherein, the error tolerance of the optimization problem is obtained, then a support vector machine is used for obtaining the optimal derivation linear parameter of the fuzzy rule of the fuzzy neural network and the forecast output of the dual optimization problem:

<math> <mrow> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>&Element;</mo> <mi>SV</mi> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>R</mi> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>&lt;</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>,</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>></mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAre each y_p-f(X_p) Lagrange multipliers corresponding to greater than 0 and less than 0,the predicted COD value corresponding to the p-th training sample and the operation variable value for making the COD discharge reach the standard are obtained.

The adaptive structure optimization module is mainly determined by artificial experience in the determination of the structural parameters of the fuzzy neural network, and once the structural parameters are determined, the whole model structure cannot be adaptively optimized. The module increases the threshold value mu by setting a fuzzy rule_th-addFuzzy rule importance reduction threshold mu_th-dFuzzy rule pruning threshold mu_th-delAnd carrying out self-adaptive adjustment on the structure of the fuzzy neural network in the process of processing the training sample. In equation (5), the fuzzy rule i is applied to the p-th training sample X_p=[X_p1,…,X_pn]Has a fitness of mu⁽ⁱ⁾(X_p) And the fuzzy rule item with the maximum fitness value in the fuzzy rules is as follows:

<math> <mrow> <mi>I</mi> <mo>=</mo> <mi>arg</mi> <munder> <mi>max</mi> <mrow> <mn>1</mn> <mo>&le;</mo> <mi>i</mi> <mo>&le;</mo> <mi>R</mi> </mrow> </munder> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinItem numbers representing fuzzy rule items of greatest fitness value, i.e.

If μ^(I)<μ_th-addThat is, the maximum value of the adaptability of the fuzzy rule is less than the set fuzzy rule increasing threshold value mu_th-addThen a new rule is added. The center and width of the gaussian membership function of the newly added fuzzy rule are:

$m_j^{\mathrm{new}}=X_{\mathrm{pj}},j=1,...,n---\left(16\right)$

<math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>j</mi> <mi>new</mi> </msubsup> <mo>=</mo> <mi>&beta;</mi> <mo>&times;</mo> <mfrac> <msup> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>Ij</mi> </msub> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>Ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAndconstant beta for the center and width of the Gaussian membership function of the new fuzzy rule>0 denotes the degree of overlap between the new fuzzy rule and the fuzzy rule I, and the value of β typically takes 1.2.

In order to prevent the number of fuzzy rules from increasing, an adaptive method is adopted to determine whether the fuzzy rules are deleted or not: if the ith fuzzy rule is applied to the p training sample⁽ⁱ⁾(X_p) Less than the fuzzy rule significance reduction threshold mu_th-dThe fuzzy rule importance is decreased and vice versa, wherein the importance of the ith fuzzy rule is D_iTo represent; if the ith rule is fuzzy D_iThe value is reduced to the fuzzy rule reduction threshold mu in the training process of the training sample_th-delThen the ith fuzzy rule is deleted.

Importance value D for each fuzzy rule_iI =1, …, and the initial value of R is set to 1, which varies with the input training sample as shown in equation (18):

wherein D_iThe importance of the ith fuzzy rule is shown, and the constant tau value determines the speed of the change of the importance of the fuzzy rule, and is 1, mu⁽ⁱ⁾(X_p) Denotes the adaptation value, μ, of the ith fuzzy rule to the p-th training sample_th-dIndicating a fuzzy rule significance reduction threshold.

When D is present_iSatisfies D_i<μ_th-delWhere μ_th-delIf 0.005 is taken, the ith fuzzy rule is deleted.

The upper computer 6 further includes: the signal acquisition module 11 is used for acquiring data from a database according to a set time interval of each sampling;

the upper computer 6 further comprises: and the discrimination model updating module 12 is used for acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actual measurement COD with a function prediction value, and if the relative error is more than 10% or the actual measurement COD data does not reach the standard, adding new data which reaches the standard when the DCS database is normally produced into training sample data and updating the model.

The system also comprises a DCS (distributed control system), wherein the DCS is composed of a data interface 3, a control station 5 and a database 4; the intelligent instrument 2, the DCS system and the upper computer 6 are sequentially connected through a field bus; the upper computer 6 also comprises a result display module 10 which is used for transmitting the COD forecast value and the operation variable value which enables the COD emission to reach the standard to the DCS, displaying the process state at a control station of the DCS and transmitting the process state information to a field operation station for displaying through the DCS and a field bus; meanwhile, the DCS system takes the obtained operation variable value which enables the COD emission to reach the standard as a new operation variable set value, and automatically executes the COD emission standard-reaching operation.

When the waste liquid incineration process is provided with the DCS system, the functions of obtaining the COD forecast value and the operation variable value for enabling the COD emission to reach the standard are mainly completed on the upper computer by utilizing the real-time and historical databases of the DCS system to detect and store the real-time dynamic data of the sample.

When the waste liquid incineration process is not equipped with a DCS system, the data memory is adopted to replace the data storage function of a real-time and historical database of the DCS system, and the functional system for obtaining the COD forecast value and the operation variable value for enabling the COD emission to reach the standard is manufactured into an independent complete system-on-chip which comprises an I/O element, a data memory, a program memory, an arithmetic unit and a display module and does not depend on the DCS system.

Example 2

Referring to fig. 1 and 2, the adaptive control method for the emission of harmful substances in the pesticide waste liquid incinerator reaches the standard, and the control method specifically comprises the following implementation steps:

1) determining key variables used by a harmful substance emission process object of the pesticide waste liquid incinerator according to process analysis and operation analysis, acquiring data of the variables during normal production from a DCS (distributed control system) database as an input matrix of a training sample TX, and acquiring corresponding COD (chemical oxygen demand) and operation variable data for enabling the COD emission to reach the standard as an output matrix Y;

2) the method is used for preprocessing the model training samples input from the DCS database, so that the mean value of the training samples is 0, the variance is 1, and the processing is completed by adopting the following formula process:

calculating an average value: <math> <mrow> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance: <math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization: <math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>TX</mi> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>&sigma;</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein, TX_iThe ith training sample is the data of key variables, Chemical Oxygen Demand (COD) and corresponding operating variables for enabling COD to be discharged when the production is normal, which are collected from a DCS database, N is the number of the training samples,is the mean of the training samples, and X is the normalized training sample. Sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples.

3) And carrying out fuzzy reasoning and establishing a fuzzy rule on the input variable transmitted from the data preprocessing module. And carrying out fuzzy classification on the preprocessed training sample X transmitted from the data preprocessing module to obtain the center and the width of each fuzzy cluster in the fuzzy rule base. Let the p-th normalized training sample X_p=[X_p1,…,X_pn]Where n is the number of input variables.

Let the fuzzy neural network have R fuzzy rules, for obtaining each fuzzy rule for training sample X_pEach input variable X of_pjJ =1, …, n, whose membership to the i-th fuzzy rule is to be found by the following fuzzy equation:

<math> <mrow> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein m is_ijAnd σ_ijAnd respectively representing the center and the width of the jth Gaussian member function of the ith fuzzy rule, and obtaining the center and the width by fuzzy clustering.

Let training sample X_pFitness to fuzzy rule i is mu⁽ⁱ⁾(X_p) Then μ⁽ⁱ⁾(X_p) Can be determined by the following formula:

<math> <mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&Pi;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mi>ij</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>ij</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

after the fitness of the input training sample to each rule is obtained, the fuzzy neural network deduces the output of the fuzzy rule to obtain the final analytic solution. In a commonly used fuzzy neural network structure, the process of deriving each fuzzy rule can be expressed as follows: firstly, the linear product sum of all input variables in the training sample is obtained, and then the linear product sum is used to match the fitness mu of the rule⁽ⁱ⁾(X_p) And multiplying to obtain the final output of each fuzzy rule. The derived output of the fuzzy rule i can be expressed as follows:

<math> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula (f)⁽ⁱ⁾For the output of the ith fuzzy rule,is the predicted output of the fuzzy neural network model to the p-th training sample, a_ijJ =1, …, n is the linear coefficient of the jth variable in the ith fuzzy rule, a_i0Is a constant term of the linear product sum of the input variables in the ith fuzzy rule, and b is an output offset.

4) In the formula (7), the determination of the parameters in the linear product sum of the input variables is a main problem used in the use of the fuzzy neural network, here, the original fuzzy rule derivation output form is converted into the optimization problem of the support vector machine, and then the linear optimization is performed by using the support vector machine, wherein the conversion process is as follows:

<math> <mrow> <mfenced open='' close=''> <mtable> <mtr> <mtd> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <mi>b</mi> </mtd> </mtr> <mtr> <mtd> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>&times;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>+</mo> <mi>b</mi> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein X_p0Is constantly equal to 1. Order to

<math> <mrow> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>]</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

Wherein,the conversion form of the original training sample is represented, namely, the original training sample is converted into the form of the formula as above, and the form is used as the training sample of the support vector machine:

<math> <mrow> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>[</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </msup> <mo>&times;</mo> <msub> <mi>X</mi> <mi>pn</mi> </msub> <mo>]</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein y is₁,…,y_NThe target output of the training sample is taken, S is taken as a new input training sample set, and then the original problem can be converted into the following dual optimization problem of the support vector machine:

<math> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&gamma;</mi> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msub> <mi>L</mi> <mi>&epsiv;</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>p</mi> </msub> <mo>,</mo> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mi>&omega;</mi> <mi>T</mi> </msup> <mi>&omega;</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow> </math>

y_pis inputting a training sample X_pTarget output of f (X)_p) Is corresponding toX_pModel output of, L (y_p,f(X_p) Is an input training sample X_pCorresponding target output y_pAnd the model output f (X)_p) The first order insensitive function when the error margin of the optimization problem is. Gamma is the penalty factor of the support vector machine, R (omega, b) is the objective function of the optimization problem, N is the number of training samples, L (y_p,f(X_p) Expression) as follows:

wherein, the error tolerance of the optimization problem is obtained, then a support vector machine is used for obtaining the optimal derivation linear parameter of the fuzzy rule of the fuzzy neural network and the forecast output of the dual optimization problem:

<math> <mrow> <msub> <mi>a</mi> <mi>ij</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>&Element;</mo> <mi>SV</mi> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mi>kj</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>R</mi> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>&lt;</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>,</mo> <mover> <mi>&phi;</mi> <mo>&RightArrow;</mo> </mover> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>></mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAre each y_p-f(X_p) Lagrange multipliers corresponding to greater than 0 and less than 0,the predicted COD value corresponding to the p-th training sample and the operation variable value for making the COD discharge reach the standard are obtained.

5) Setting fuzzy rule increasing threshold value mu_th-addFuzzy rule importance reduction threshold mu_th-dFuzzy rule pruning threshold mu_th-delAnd carrying out self-adaptive adjustment on the structure of the fuzzy neural network in the process of processing the training sample. In equation (5), the fuzzy rule i is applied to the p-th training sample X_p=[x₁,…,x_n]Has a fitness of mu⁽ⁱ⁾(X_p) And the fuzzy rule item with the maximum fitness value in the fuzzy rules is as follows:

<math> <mrow> <mi>I</mi> <mo>=</mo> <mi>arg</mi> <munder> <mi>max</mi> <mrow> <mn>1</mn> <mo>&le;</mo> <mi>i</mi> <mo>&le;</mo> <mi>R</mi> </mrow> </munder> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinItem numbers representing fuzzy rule items of greatest fitness value, i.e.

If μ^(I)<μ_th-addThat is, the maximum value of the adaptability of the fuzzy rule is less than the set fuzzy rule increasing threshold value mu_th-addThen a new rule is added. The center and width of the gaussian membership function of the newly added fuzzy rule are:

$m_j^{\mathrm{new}}=X_{\mathrm{pj}},j=1,...,n---\left(16\right)$

<math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>j</mi> <mi>new</mi> </msubsup> <mo>=</mo> <mi>&beta;</mi> <mo>&times;</mo> <mfrac> <msup> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>X</mi> <mi>pj</mi> </msub> <mo>-</mo> <msub> <mi>m</mi> <mi>Ij</mi> </msub> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mi>Ij</mi> <mn>2</mn> </msubsup> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAndconstant beta for the center and width of the Gaussian membership function of the new fuzzy rule>0 denotes the degree of overlap between the new fuzzy rule and the fuzzy rule I, and the value of β typically takes 1.2.

In order to prevent the number of fuzzy rules from increasing, an adaptive method is adopted to determine whether the fuzzy rules are deleted or not: if the ith fuzzy rule is applied to the p training sample⁽ⁱ⁾(X_p) Less than the fuzzy rule significance reduction threshold mu_th-dThe fuzzy rule importance is decreased and vice versa, wherein the importance of the ith fuzzy rule is D_iTo represent; if the ith rule is fuzzy D_iThe value is reduced to the fuzzy rule reduction threshold mu in the training process of the training sample_th-delThen the ith fuzzy rule is deleted.

Importance value D for each fuzzy rule_iI =1, …, and the initial value of R is set to 1, which varies with the input training sample as shown in equation (18):

wherein D_iThe importance of the ith fuzzy rule is shown, and the constant tau value determines the speed of the change of the importance of the fuzzy rule, and is 1, mu⁽ⁱ⁾(X_p) Denotes the adaptation value, μ, of the ith fuzzy rule to the p-th training sample_th-dIndicating a fuzzy rule significance reduction threshold.

When D is present_iSatisfies D_i<μ_th-delWhere μ_th-delIf 0.005 is taken, the ith fuzzy rule is deleted.

The method further comprises the following steps: 6) and acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actual measurement COD with a function forecast value, and if the relative error is more than 10% or the actual measurement COD data does not reach the standard, adding new data which reaches the standard when the data is normally produced in the DCS database into training sample data, and updating the model.

7) In the step 4), transmitting the COD forecast value and the operation variable value which enables the COD emission to reach the standard to a DCS system, displaying the process state at a control station of the DCS, and transmitting the process state information to a field operation station for displaying through the DCS system and a field bus; meanwhile, the DCS system takes the obtained operation variable value which enables the COD emission to reach the standard as a new operation variable set value, and automatically executes the COD emission standard-reaching operation.

The key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator, and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.

Claims (2)

1. A self-adaptive pesticide waste liquid incinerator harmful substance emission standard-reaching control system comprises an incinerator, an on-site intelligent instrument, a DCS (distributed control system), a data interface and an upper computer, wherein the DCS comprises a control station and a database; on-spot intelligent instrument and DCS headtotail, the DCS system is connected with the host computer, its characterized in that: the host computer include:

the data preprocessing module is used for preprocessing the model training samples input from the DCS database, so that the mean value of the training samples is 0, and the variance of the training samples is 1, and the processing is completed by adopting the following formula:

calculating an average value: <math> <mrow> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance: <math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization: <math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>T</mi> <mi>X</mi> <mo>-</mo> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>&sigma;</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein TX is a training sample, TX_iThe ith training sample is the data of key variables, Chemical Oxygen Demand (COD) and corresponding operating variables for enabling COD to be discharged when the production is normal, which are collected from a DCS database, N is the number of the training samples,is the mean value of the training sample, and X is the training sample after standardization; sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples;

the fuzzy neural network module is used for carrying out fuzzy reasoning and establishing a fuzzy rule on the input variable transmitted from the data preprocessing module; carrying out fuzzy classification on the preprocessed training sample X transmitted from the data preprocessing module to obtain the center and the width of each fuzzy cluster in a fuzzy rule base; let the p-th normalized training sample X_p＝[X_p1,...,X_pn]Where n is the number of input variables;

let the fuzzy neural network have R fuzzy rules, for obtaining each fuzzy rule for training sample X_pEach input variable X of_pjJ is 1, …, n, and the following blurring equation will find its membership to the ith fuzzy rule:

<math> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein m is_ijAnd σ_ijRespectively representing the center and the width of a jth Gaussian member function of the ith fuzzy rule, and obtaining the center and the width by fuzzy clustering;

let training sample X_pFitness to fuzzy rule i is mu⁽ⁱ⁾(X_p) Then μ⁽ⁱ⁾(X_p) Can be determined by the following formula:

<math> <mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Pi;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

after the fitness of the input training sample to each rule is obtained, the fuzzy neural network deduces the output of the fuzzy rule to obtain the final analytic solution; in a commonly used fuzzy neural network structure, the process of deriving each fuzzy rule can be expressed as follows: firstly, the linear product sum of all input variables in the training sample is obtained, and then the linear product sum is used to match the fitness mu of the ruleⁱ(X_p) Multiplying to obtain the final output of each fuzzy rule; the derived output of the fuzzy rule i can be expressed as follows:

<math> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>&lsqb;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula (f)⁽ⁱ⁾For the output of the ith fuzzy rule,is the predicted output of the fuzzy neural network model to the p-th training sample, a_ijJ is 1, …, n is the linear coefficient of the jth variable in the ith fuzzy rule, a_i0Is the constant term of the input variable linear product sum in the ith fuzzy rule, and b is the output offset;

in formula (7), the determination of parameters in the input variable linear product sum is a main problem used in the use of the fuzzy neural network, here, the original fuzzy rule derivation output form is converted into the support vector machine optimization problem, and then the support vector machine is used for linear optimization, wherein the conversion process is as follows:

<math> <mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>&lsqb;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>+</mo> <mi>b</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>r</mi> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <mi>b</mi> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein X_p0Is a constant term and is constant equal to 1; order to

Wherein,the conversion form of the original training sample is represented, namely, the original training sample is converted into the form of the formula as above, and the form is used as the training sample of the support vector machine:

wherein y is₁,…,y_NThe target output of the training sample is taken, S is taken as a new input training sample set, and then the original problem can be converted into the following dual optimization problem of the support vector machine:

<math> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&gamma;</mi> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msub> <mi>L</mi> <mi>&epsiv;</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>p</mi> </msub> <mo>,</mo> <mi>f</mi> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mi>&omega;</mi> <mi>T</mi> </msup> <mi>&omega;</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow> </math>

y_pis inputting a training sample X_pTarget output of f (X)_p) Is corresponding to X_pModel output of, L (y_p,f(X_p) Is an input training sample X_pCorresponding target output y_pAnd the model output f (X)_p) A first order insensitive function when the error tolerance of the optimization problem is; gamma is the penalty factor of the support vector machine, R (omega, b) is the objective function of the optimization problem, N is the number of training samples, L (y_p,f(X_p) Expression) as follows:

wherein, the error tolerance of the optimization problem is obtained, then a support vector machine is used for obtaining the optimal derivation linear parameter of the fuzzy rule of the fuzzy neural network and the forecast output of the dual optimization problem:

<math> <mrow> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>&Element;</mo> <mi>S</mi> <mi>V</mi> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>R</mi> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein alpha is_k，Are each y_p-f(X_p) Lagrange multipliers corresponding to greater than 0 and less than 0,the COD forecast value corresponding to the p-th training sample and the operation variable value for making the COD discharge reach the standard are obtained;

the self-adaptive structure optimization module is mainly determined by artificial experience in the determination of the structural parameters of the fuzzy neural network, and once the structural parameters are determined, the whole model structure cannot be subjected to self-adaptive optimization; the module increases the threshold value mu by setting a fuzzy rule_th-addFuzzy rule importance reduction threshold mu_th-dFuzzy rule pruning threshold mu_th-delCarrying out self-adaptive adjustment on the structure of the fuzzy neural network in the process of processing the training sample; in equation (5), the fuzzy rule i is applied to the p-th training sample X_p＝[X_p1,...,X_pn]Has a fitness of mu⁽ⁱ⁾(X_p) And the fuzzy rule item with the maximum fitness value in the fuzzy rules is as follows:

<math> <mrow> <mi>I</mi> <mo>=</mo> <mi>arg</mi> <munder> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mn>1</mn> <mo>&le;</mo> <mi>i</mi> <mo>&le;</mo> <mi>R</mi> </mrow> </munder> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinItem numbers representing fuzzy rule items of greatest fitness value, i.e.

If μ^(I)＜μ_th-addThat is, the maximum value of the adaptability of the fuzzy rule is less than the set fuzzy rule increasing threshold value mu_th-addAdding a new rule; the center and width of the gaussian membership function of the newly added fuzzy rule are:

$m_j^{new}=X_{pj},j=1,...,n---\left(16\right)$

<math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>j</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> </msubsup> <mo>=</mo> <mi>&beta;</mi> <mo>&times;</mo> <mfrac> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>m</mi> <mrow> <mi>I</mi> <mi>j</mi> </mrow> </msub> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> <msubsup> <mi>&sigma;</mi> <mrow> <mi>I</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAndthe center and the width of a Gaussian member function of the new fuzzy rule are represented, a constant beta is more than 0 to represent the overlapping degree between the new fuzzy rule and the fuzzy rule I, and the beta value is 1.2; in order to prevent the number of fuzzy rules from increasing, an adaptive method is adopted to determine whether the fuzzy rules are deleted or not: if the ith fuzzy rule is applied to the p training sample⁽ⁱ⁾(X_p) Less than the fuzzy rule significance reduction threshold mu_th-dThe fuzzy rule importance is decreased and vice versa, wherein the importance of the ith fuzzy rule is D_iTo represent; if the ith rule is fuzzy D_iThe value is reduced to the fuzzy rule reduction threshold mu in the training process of the training sample_th-delIf yes, deleting the ith fuzzy rule;

importance value D for each fuzzy rule_iThe initial value of R is set to 1, …, and the variation process of R with the input training sample is shown as equation (18):

wherein D_iThe importance of the ith fuzzy rule is shown, and the constant tau value determines the speed of the change of the importance of the fuzzy rule, and is 1, mu⁽ⁱ⁾(X_p) Denotes the adaptation value, μ, of the ith fuzzy rule to the p-th training sample_th-dRepresenting a fuzzy rule significance reduction threshold;

when D is present_iSatisfies D_i＜μ_th-delWhere μ_th-delTaking 0.005, deleting the ith fuzzy rule;

the host computer still include: the judging model updating module is used for acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured chemical oxygen consumption with a function forecast value, and if the relative error is more than 10% or the actually-measured COD data does not reach the standard, adding new data which reaches the standard when the DCS database is normally produced into training sample data and updating the model;

the result display module is used for transmitting the COD forecast value and the operation variable value which enables the COD emission to reach the standard to the DCS, displaying the values at a control station of the DCS and transmitting the values to a field operation station for displaying through the DCS and a field bus; meanwhile, the DCS system takes the obtained operation variable value which enables the COD emission to reach the standard as a new operation variable set value, and automatically executes the COD emission standard-reaching operation;

the signal acquisition module is used for acquiring data from the database according to the set time interval of each sampling;

the key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.

2. A control method implemented by the adaptive pesticide waste liquid incinerator harmful substance emission standard control system according to claim 1, characterized in that: the method comprises the following concrete implementation steps:

1) determining key variables used by a Chemical Oxygen Demand (COD) discharge process object of the pesticide waste liquid incinerator according to process analysis and operation analysis, collecting data of the variables during normal production from a Distributed Control System (DCS) database as an input matrix of a training sample (TX), and collecting corresponding COD (chemical oxygen demand) and operation variable data for enabling COD discharge to reach the standard as an output matrix Y;

2) the method is used for preprocessing the model training samples input from the DCS database, so that the mean value of the training samples is 0, the variance is 1, and the processing is completed by adopting the following formula process:

calculating an average value: <math> <mrow> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance: <math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization: <math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>T</mi> <mi>X</mi> <mo>-</mo> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>&sigma;</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein TX is a training sample, TX_iThe ith training sample is the data of key variables, Chemical Oxygen Demand (COD) and corresponding operating variables for enabling COD to be discharged when the production is normal, which are collected from a DCS database, N is the number of the training samples,is the mean value of the training sample, and X is the training sample after standardization; sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples;

3) carrying out fuzzy reasoning and establishing a fuzzy rule on the input variable transmitted from the data preprocessing module; carrying out fuzzy classification on the preprocessed training sample X transmitted from the data preprocessing module to obtain the center and the width of each fuzzy cluster in a fuzzy rule base; let the p-th normalized training sample X_p＝[X_p1,...,X_pn]Where n is the number of input variables;

let the fuzzy neural network have R fuzzy rules, for obtaining each fuzzy rule for training sample X_pEach input variable X of_pjJ is 1, …, n, and the following blurring equation will find its membership to the ith fuzzy rule:

<math> <mrow> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein m is_ijAnd σ_ijRespectively representing the center and the width of a jth Gaussian member function of the ith fuzzy rule, and obtaining the center and the width by fuzzy clustering;

let training sample X_pFitness to fuzzy rule i is mu⁽ⁱ⁾(X_p) Then μ⁽ⁱ⁾(X_p) Can be determined by the following formula:

<math> <mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Pi;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mo>{</mo> <mo>-</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>m</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <msubsup> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mfrac> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

after the fitness of the input training sample to each rule is obtained, the fuzzy neural network deduces the output of the fuzzy rule to obtain the final analytic solution; in a commonly used fuzzy neural network structure, the process of deriving each fuzzy rule can be expressed as follows: firstly, the linear product sum of all input variables in the training sample is obtained, and then the linear product sum is used to match the fitness mu of the rule⁽ⁱ⁾(X_p) Multiplying to obtain the final output of each fuzzy rule; the derived output of the fuzzy rule i can be expressed as follows:

<math> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>&lsqb;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>+</mo> <mi>b</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula (f)⁽ⁱ⁾For the output of the ith fuzzy rule,is the predicted output of the fuzzy neural network model to the p-th training sample, a_ijJ is 1, …, n is the linear coefficient of the jth variable in the ith fuzzy rule, a_i0Is the constant term of the input variable linear product sum in the ith fuzzy rule, and b is the output offset;

4) in the formula (7), the determination of the parameters in the linear product sum of the input variables is a main problem used in the use of the fuzzy neural network, here, the original fuzzy rule derivation output form is converted into the optimization problem of the support vector machine, and then the linear optimization is performed by using the support vector machine, wherein the conversion process is as follows:

<math> <mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mrow> <msup> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>R</mi> </munderover> <mo>&lsqb;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>+</mo> <mi>b</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>r</mi> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&times;</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>&times;</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <mi>b</mi> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein X_p0Is a constant term and is constant equal to 1; order to

Wherein,the conversion form of the original training sample is represented, namely, the original training sample is converted into the form of the formula as above, and the form is used as the training sample of the support vector machine:

wherein y is₁,…,y_NThe target output of the training sample is taken, S is taken as a new input training sample set, and the original problem can be converted into the following dual optimization of the support vector machineThe problems are as follows:

<math> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&gamma;</mi> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msub> <mi>L</mi> <mi>&epsiv;</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>p</mi> </msub> <mo>,</mo> <mi>f</mi> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mi>&omega;</mi> <mi>T</mi> </msup> <mi>&omega;</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow> </math>

y_pis inputting a training sample X_pTarget output of f (X)_p) Is corresponding to X_pModel output of, L (y_p,f(X_p) Is an input training sample X_pCorresponding target output y_pAnd the model output f (X)_p) A first order insensitive function when the error tolerance of the optimization problem is; gamma is the penalty factor of the support vector machine, R (omega, b) is the objective function of the optimization problem, N is the number of training samples, L (y_p,f(X_p) Expression) as follows:

wherein, the error tolerance of the optimization problem is obtained, then a support vector machine is used for obtaining the optimal derivation linear parameter of the fuzzy rule of the fuzzy neural network and the forecast output of the dual optimization problem:

<math> <mrow> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>&Element;</mo> <mi>S</mi> <mi>V</mi> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msubsup> <mi>&alpha;</mi> <mi>k</mi> <mo>*</mo> </msubsup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>R</mi> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein alpha is_k，Are each y_p-f(X_p) Lagrange multipliers corresponding to greater than 0 and less than 0,the COD forecast value corresponding to the p-th training sample and the operation variable value for making the COD discharge reach the standard are obtained;

5) setting fuzzy rule increasing threshold value mu_th-addFuzzy rule importance reduction threshold mu_th-dFuzzy rule pruning threshold mu_th-delCarrying out self-adaptive adjustment on the structure of the fuzzy neural network in the process of processing the training sample; in equation (5), the fuzzy rule i is applied to the p-th training sample X_p＝[x₁,...,x_n]Has a fitness of mu⁽ⁱ⁾(X_p) And the fuzzy rule item with the maximum fitness value in the fuzzy rules is as follows:

<math> <mrow> <mi>I</mi> <mo>=</mo> <mi>arg</mi> <munder> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mn>1</mn> <mo>&le;</mo> <mi>i</mi> <mo>&le;</mo> <mi>R</mi> </mrow> </munder> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinIndicating fitnessItem number of fuzzy rule item with maximum value, i.e.

If μ^(I)＜μ_th-addThat is, the maximum value of the adaptability of the fuzzy rule is less than the set fuzzy rule increasing threshold value mu_th-addAdding a new rule; the center and width of the gaussian membership function of the newly added fuzzy rule are:

$m_j^{new}=X_{pj},j=1,...,n---\left(16\right)$

<math> <mrow> <msubsup> <mi>&sigma;</mi> <mi>j</mi> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> </msubsup> <mo>=</mo> <mi>&beta;</mi> <mo>&times;</mo> <mfrac> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>X</mi> <mrow> <mi>p</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>m</mi> <mrow> <mi>I</mi> <mi>j</mi> </mrow> </msub> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> <msubsup> <mi>&sigma;</mi> <mrow> <mi>I</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mfrac> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow> </math>

whereinAndthe center and the width of a Gaussian member function of the new fuzzy rule are represented, a constant beta is more than 0 to represent the overlapping degree between the new fuzzy rule and the fuzzy rule I, and the beta value is 1.2;

in order to prevent the number of fuzzy rules from increasing, an adaptive method is adopted to determine whether the fuzzy rules are deleted or not: if the ith fuzzy rule is applied to the p training sample⁽ⁱ⁾(X_p) Less than the fuzzy rule significance reduction threshold mu_th-dThe fuzzy rule importance is decreased and vice versa, wherein the importance of the ith fuzzy rule is D_iTo represent; if the ith rule is fuzzy D_iThe value is reduced to the fuzzy rule reduction threshold mu in the training process of the training sample_th-delIf yes, deleting the ith fuzzy rule;

importance value D for each fuzzy rule_iThe initial value of R is set to 1, …, and the variation process of R with the input training sample is shown as equation (18):

wherein D_iThe importance of the ith fuzzy rule is shown, and the constant tau value determines the speed of the change of the importance of the fuzzy rule, and is 1, mu⁽ⁱ⁾(X_p) Denotes the adaptation value, μ, of the ith fuzzy rule to the p-th training sample_th-dRepresenting a fuzzy rule significance reduction threshold;

when D is present_iSatisfies D_i＜μ_th-delWhere μ_th-delTaking 0.005, deleting the ith fuzzy rule;

the method further comprises the following steps: 6) acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured chemical oxygen demand emission value with a function forecast value, and if the relative error is more than 10% or the actually-measured COD data does not reach the standard, adding new data which reaches the standard when the data is normally produced in a DCS database into training sample data, and updating the model;

7) transmitting the result of the COD forecast value obtained in the step 4) and the operation variable value which enables the COD emission to reach the standard to a DCS (distributed control system), displaying the result on a control station of the DCS, and transmitting the result to a field operation station for displaying through the DCS and a field bus; meanwhile, the DCS system takes the obtained operation variable value which enables the COD emission to reach the standard as a new operation variable set value, and automatically executes the COD emission standard-reaching operation;

the key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.