CN103729569A - Soft measurement system for flue gas of power-station boiler on basis of LSSVM (Least Squares Support Vector Machine) and online updating - Google Patents

Abstract

The invention provides a soft measurement system for flue gas of a power-station boiler on the basis of a least squares support vector machine and online updating and belongs to the fields of a thermal engineering technology and an artificial intelligence crossing technology. According to the soft measurement system, by selecting operation and state parameters related to the power-station boiler as input of a model, the content of flue gas to be predicted as output of the model, and historical operation data as an initial training sample, and an initial model for flue-gas emission is established by utilizing the least squares support vector machine; and in addition, on the basis of analysis on time-variable characteristics in flue-gas emission, an updating strategy based on sample replacement and sample addition is proposed, and solving of the parameters and updating of the model are realized by adopting two modes of deleting samples and adding samples in the form of increment. The soft measurement system provided by the invention has the advantages that the performance of the model is improved along with the change of the process characteristic in a self-adaptive manner, so that accurate predication to the flue-gas emission can be realized and important significance for safe and optimal operation of the power-station boiler is achieved.

Description

The soft measuring system of a kind of flue gas in power station boiler based on LSSVM and online updating

Technical field

The present invention relates to a kind of soft measuring system of flue gas in power station boiler based on least square method supporting vector machine (least squares support vector machine, LSSVM) and online updating, belong to heat power engineering and artificial intelligence interleaving techniques field.

Background technology

In order to guarantee the safety of station boiler and to optimize operation, usually need to obtain unburned carbon in flue dust and NOx in boiler tail flue gas and discharge isoparametric relevant information.At present, these parameters are often utilized flying dust carbonmeter and fume continuous monitoring system (continuous emission monitoring system, CEMS) etc. hardware sensor is measured, but the installation and maintenance cost of these instruments is higher, and owing to being operated in severe electromagnetic environment, often need off-line maintenance.Therefore, adopt other boiler operatiopn and state parameters of easily surveying by certain numerical relationship model, smoke components content to be predicted to there is important engineering significance.Due to complicacy and the uncertainty of combustion process, set up that mechanism model is very difficult often accurately.In recent years, the informationization in power station makes obtaining of process operation data more and more easier, and the soft-measuring technique developing into based on data of the artificial intelligence such as neural network, support vector machine provides effective instrument.Wherein, least square method supporting vector machine (least squares support vector machine, LSSVM) be take structural risk minimization as principle, compares have better generalization ability with neural network.And LSSVM utilizes equality constraint to replace inequality constrain, and problem concerning study is converted into and solves system of linear equations, has reduced the complexity of algorithm.

When utilizing the methods such as LSSVM to build flue gas soft-sensing model, the screening of initial sample is extremely important, should make as much as possible it cover full operating mode when choosing initial training sample from history data storehouse.Yet in fact, what store in database is mostly normal operating condition, artificially do not regulate on one's own initiative and set each thermal parameter, be therefore difficult to guarantee that selected sample can cover all condition ranges.After model is set up, in operational process, the change of operational order and adjusting parameter may bring new operating mode, and model cannot carry out accurately predicting to smoke components content.On the other hand, in operational process, the variation of ature of coal and the wearing and tearing of equipment and maintenance also can cause the transition of fume emission characteristic, the initial model of setting up precision of prediction after operation a period of time can decline gradually, if rebuild model, heavy computation burden can be brought, and the useful information existing in master mould can be abandoned.Therefore, utilize model modification to improve its performance, to realizing the accurate measurement of smoke components content, have great significance.

Summary of the invention

The object of the invention is to overcome the time-varying characteristics of existing fume emission, proposed the soft measuring system of a kind of flue gas in power station boiler based on LSSVM and online updating.

Generally speaking, in thermal process, the variation of fume emission characteristic is mainly caused by the factor of two aspects: (1) enters the factors such as the variation of stove ature of coal and the wearing and tearing of equipment and maintenance in operational process, causes process characteristic to change; The variation of this specific character is irreversible, is also can not return previous running status after characteristic variations.(2) thus due to production operation instruction and regulate the change of parameter to occur the work condition state that some are new; This characteristic variations is reversible, because along with regulating the continuation of parameter to change, process is likely switched to existing state previous historical operating mode from present condition.For this two specific character, change, corresponding model update method is also different.For the first characteristic variations, need to delete old sample information.This is because old sample is the description to previous operational process, and irreversible change has occurred operational process, and these samples, just without any value, need to substitute with new sample, and the form that should replace with sample the renewal of this variation realizes.It is change and the switching of process normal operating condition that the second changes, and therefore new samples information need to be dissolved in old sample, with this, carrys out the working range of Extended Model, and the form that should append with sample the renewal of this variation realizes.

Therefore, the present invention proposes to build initial fume emission model by LSSVM, then utilizes sample to append the incremental update of replacing implementation model with sample.The method precision of prediction is high, cost is low, computing velocity is fast, is conducive to be applied among engineering practice.

The soft measuring system of flue gas in power station boiler based on LSSVM and online updating, described system comprises:

1) LSSVM model is set up unit: collect initial training sample and build LSSVM model, wherein: by sensor measurement generator power, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening signal, and measured value is deposited in DCS historical data base; Select above-mentioned measured value as the input variable of soft-sensing model, the smoke components content that predict, as the output variable of model, is chosen some sections of large and representative operating modes of coverage as initial training sample from history data storehouse, is designated as

x wherein _i∈ R ^prepresent i group input sample, corresponding to the generator power of measuring, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening, y _i∈ R is i group output sample, and corresponding to the content of smoke components, p is input variable number, and n is sample size, and builds LSSVM model;

LSSVM model can be described as following optimization problem:

$\underset{w,b,\mathrm{\ξ}}{\min }J(w,\mathrm{\ξ})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\γ}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{{\mathrm{\ξ}}_i}^2---\left(1\right)$

Wherein, J is objective function,

be nuclear space mapping function, w is weight vectors, and γ is penalty coefficient, ξ _ifor error variance, b is deviation, the transposition of subscript T representing matrix; For separating this optimization problem, definition Lagrange function is as follows:

Wherein, α=[α ₁..., α _n] ^tfor Lagrange multiplier; Utilize Lagrange function to ask local derviation to each variable, and to make derivative value be zero can obtain:

Cancellation intermediate variable w and ξ _i, be translated into and solve system of linear equations:

$\left[\begin{array}{cc}0& {\stackrel{\→}{I}}^T\\ \stackrel{\→}{I}& \mathrm{\Ω}+1/\mathrm{\γI}\end{array}\right]\left[\begin{array}{c}b\\ a\end{array}\right]=\left[\begin{array}{c}0\\ y\end{array}\right]---\left(4\right)$

Y=[y wherein ₁..., y _n] ^t, i is n * n rank unit matrixs, Ω={ Ω _k| k, l=1 ..., n}, and be defined as kernel function; The value that obtains α and b by solving equation group is:

$\left\{\begin{array}{c}b=\frac{{\stackrel{\→}{I}}^T{H}^{-1}y}{\stackrel{\→}{I}{H}^{-1}\stackrel{\→}{I}}\\ a=H^{-1}(y-\stackrel{\→}{I}b)\end{array}\right.---\left(5\right)$

Wherein for eigenmatrix;

Thereby the soft measurement LSSVM model that obtains initial flue gas content is:

$h\left(x\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\α}}_{i}K(x,x_i)+b---\left(6\right)$

Its Kernel Function is chosen for Gaussian radial basis function K (x, x _i)=exp (|| x-x _i|| ²/ σ ²), σ is kernel functional parameter, h (x) is the predicted value of smoke components content;

2) smoke components content prediction unit: the model that utilizes LSSVM model to set up to set up unit predicts smoke components content, is also about to generator power that sensor newly record, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening data as input variable x _q, utilize above formula to obtain the soft measured value of smoke components content

3) sample predicated error computing unit: as the measurement value sensor y of actual smoke components content _qafter collecting, calculate sample (x _q, y _q) predicated error Er:

$\mathrm{Er}=\left|\frac{{\hat{y}}_q-y_q}{y_q}\right|\×100\%---\left(7\right)$

4) predicated error judging unit: judgement predicated error: if Er> Δ, Δ is error threshold, enters nearest sample point and chooses unit; Otherwise need to judge whether test sample book finishes, if finish system end of run, otherwise enter smoke components content prediction unit;

5) sample point is chosen unit recently: from history data, choose apart from new sample (x _q, y _q) nearest sample point (x _k, y _k):

$k=\underset{i}{\arg }\underset{i=1,...,n}{\min }{\left|\right|x_q-x_i\left|\right|}_2---\left(8\right)$

6) updating type identifying unit: the data sample to new sampling judges, determines updating type according to following criterion:

(i) if || x _k-x _q|| ₂> δ ₁, model is implemented to sample and appends renewal, directly by new sample (x _q, y _q) join in previous historical data base;

(ii) if || x _k-x _q|| ₂≤ δ ₁, model is implemented to sample and replace renewal, use new sample (x _q, y _q) replace in previous historical data base and satisfy condition || x _k-x _q|| ₂≤ δ ₂similar sample;

δ wherein ₁by the mean distance between historical training data sample, determined δ ₂be made as 0.5 δ ₁;

7) eigenmatrix updating block: according to definite updating type, the initial LSSVM model of LSSVM model being set up to unit acquisition carries out incremental update, to eigenmatrix H ^-1calculating upgrade, wherein, update strategy is decomposed into sample increases and sample is deleted two kinds of patterns: according to updating type, determine definite scheme of subelement, if enforcement sample appends renewal, directly carry out sample increase; If implement sample, replace renewal, first carry out relevant sample and delete, and then carry out sample increase;

(i) sample is deleted pattern

The sample that summary is deleted is (x _s, y _s), n in exchange features matrix H is capable and s is capable, after n row and s row, obtains new eigenmatrix to be:

Wherein $k=K(x_s,x_s)+\frac{1}{\mathrm{\γ}},$ k _s=[K(x ₁,x _s),…,K(x _s-1,x _s),K(x _n,x _s),K(x _s+1,x _s),…,K(x _n-1,x _s)] ^T；

If note exchange n is capable and s is capable:

n row and s row:

corresponding elementary matrix is respectively

with

have

and:

$H_0^{-1}=T_{n,s}^{-1}{H}^{-1}{T}_{n,s}^{-1}=T_{n,s}{H}^{-1}{T}_{n,s}---\left(10\right)$

If H ₀inverse matrix be designated as:

$H_0^{-1}\stackrel{\mathrm{\Δ}}{=}\left[\begin{array}{cc}{h}_{11}& h_{12}\\ h_{12}^T& h_{22}\end{array}\right]---\left(11\right)$

, according to piecemeal finding the inverse matrix, can obtain:

$H_0={\left[\begin{array}{cc}{h}_{11}& h_{12}\\ h_{12}^T& h_{22}\end{array}\right]}^{-1}=\left[\begin{array}{cc}{(h_{11}-h_{12}{h}_{22}^{-1}{h}_{22}^T)}^{-1}& *\\ *& *\end{array}\right]---\left(12\right)$

Contrast and formula can obtain new eigenmatrix H ₁inverse matrix be:

$H_1^{-1}=h_{11}-h_{12}{h}_{22}^{-1}{h}_{12}^T---\left(13\right)$

Here only provide and delete single sample (x _s, y _s) situation, if will delete a plurality of samples, carry out successively;

(ii) sample increases pattern

The sample that summary increases is (x _t, y _t), the eigenmatrix of still remembering "current" model is H, the eigenmatrix H under new samples ₂can be designated as:

Wherein $k_t={[K(x_1,x_t),K(x_2,x_t),...,K(x_n,x_t)]}^T,k=K(x_t,x_t)+\frac{1}{\mathrm{\γ}};$

Inversion Formula by piecemeal square can obtain H ₂contrary be:

$H_2^{-1}=\left[\begin{array}{cc}{H}^{-1}+H^{-1}{k}_t{\mathrm{\ρ}}^{-1}{k}_t^T{H}^{-1}& -H^{-1}{k}_t{\mathrm{\ρ}}^{-1}\\ -{\mathrm{\ρ}}^{-1}{k}_t^T{H}^{-1}& {\mathrm{\ρ}}^{-1}\end{array}\right]---\left(15\right)$

Wherein $\mathrm{\ρ}=k-k_t^T{H}^{-1}{k}_t.$

8) soft-sensing model updating block: by the new H trying to achieve ^-1bring formula into, obtain corresponding model parameter α and b, realize the renewal to flue gas soft-sensing model h (x);

The present invention utilizes LSSVM to build initial flue gas soft-sensing model, and to model online updating, has reduced the computation complexity of model, is conducive to Project Realization, can calculate to a nicety to each composition of boiler smoke.The present invention combines LSSVM modeling method with incremental update, have following significant advantage:

1) select power station generator power, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening as the input of model, can comprehensively describe boiler smoke emission performance;

2) from history data storehouse, choose some sections of large and representative operating modes of coverage and build the initial flue gas soft-sensing model of LSSVM as training sample, there is higher precision of prediction;

3) update strategy is divided into sample and appends and sample replacement, the essence changing for emission performance is carried out Renewal model;

4) utilize the method for increment to implement to upgrade, reduced the complexity of calculating;

5) application the present invention, does not increase any hardware, and is easy to engineering site application, and cost is low, predicts the outcome accurately reliable.

Accompanying drawing explanation

Fig. 1 is the variation of fume emission characteristic and corresponding Sample Refreshment process;

Fig. 2 is the structural representation of certain station boiler;

Fig. 3 is the structured flowchart of system of the present invention;

Fig. 4 utilizes the present invention to carry out the soft contrast schematic diagram that predicts the outcome measuring to the content of NOx in certain fire coal boiler fume discharge.Wherein, first 1100 groups is initial training sample, and latter 270 groups is test sample book.

Embodiment

Below in conjunction with drawings and Examples, the present invention is elaborated.The present embodiment is implemented take technical solution of the present invention under prerequisite, but protection scope of the present invention is not limited to following embodiment.

The present embodiment carries out soft measurement to the content of NOx in certain 660MW flue gas in power station boiler discharge.With reference to figure 1, consider the variation of a single-input single-output fume emission characteristic and corresponding Sample Refreshment process, the sample in Fig. 1 (a) in work condition state I is the representative initial sample of selecting from historical data base, sample space is x ∈ [x ₁, x ₂], and the initial LSSVM fume emission model y=f (x) of the Sample Establishing based in I.In operational process, regulate the change of parameter to bring new operating mode, running status will be transformed into state I I and state I II(as shown in dotted arrow in figure), now sample is at x ∈ [x ₃, x ₄] and x ∈ [x ₅, x ₆] in scope.And this state conversion is reversible, that is to say, along with the continuation of process, operating condition likely comes back to state I.Therefore, the sample in work condition state II and III need to be appended in original state I, the discharge model of being set up by original state I is expanded, make it cover larger condition range, thereby complete the renewal of model.Model after expansion, as shown in Fig. 1 (b), can find out, after upgrading, model is by initial launch scope x ∈ [x ₁, x ₂] be extended to x ∈ [x ₁, x ₆].Shown in Fig. 1 (c), emission performance is at initial operating mode x ∈ [x ₁, x ₂] on there is irreversible change, from work condition state I, change to state I V(as shown in solid arrow figure).The reason that causes this variation is likely that change has occurred the extraneous factors such as the wearing and tearing of equipment or ature of coal, and it is irreversible that this operating mode changes.At this moment need to replace old sample with the new sample gathering and upgrade with implementation model, can describe new process characteristic.Consider Fig. 1 (a) and (c) in change in process, the fume emission model after renewal and sample distribution are as shown in Fig. 1 (d).

Fig. 2 is the structural representation of certain coal-burning boiler.As shown in Figure 2, boiler form is single burner hearth Π type boiler, and adopts tangential firing mode, to obtain along the comparatively uniform aerodynamic field of burner hearth horizontal section.Main burner divides upper and lower two groups of layouts, and pulls open certain distance, reduces burner region thermal load, effectively reduces the coking of burner hearth.Above the coal nozzle of upper strata, be furnished with four layers of separated after-flame wind (SOFA) nozzle, with the needed air of postcombustion after burning.Fume continuous monitoring system (continuous emission monitoring system, CEMS) is housed in back-end ductwork, is used for measuring the content of NOx in fume emission.But CEMS in the course of the work, often needs off-line maintenance, therefore, for safety and the optimization operation of boiler, the soft-sensing model that need to build NOx discharge carries out redundant measurement.

According to Related Mechanism analysis, select affect the following parameter of emission of NOx of boiler as the input variable of model: select generator power to describe the impact of loading, by 6 coal pulverizer coal-supplying amounts, 6 coal pulverizer inlet primary air flows and 6 fuel air aperture signals carry out principal component analysis (PCA), and the feature main variables that utilizes extraction to obtain is described primary wind and powder amount along the impact of stove high score pairing NOx discharge, select 8 secondary air register aperture (AA, AB, BC, CC, DD, DE, EF, FF) impact of secondary air distribution mode on NOx discharge described, select 4 grate firings throttle opening (UA to the greatest extent, UB, UC, UD) impact of after-flame wind is described.The runtime value of all parameters by sensor measurement and deposit in DCS historical data base, is also in the historical data base of power station, all to have corresponding measuring point above.

From historical data base, choose the service data of above each parameter of continuous a week of large (from 300MW to 660MW) of unit load span, the sampling period is 60s.After data are cleaned, be divided into two groups: wherein 1100 groups as initial training sample, and other 270 groups of operating mode sections of not participating in training are as test sample book.

Please refer to Fig. 3, the block diagram of the soft measuring system of NOx in the flue gas in power station boiler based on LSSVM and online updating that the present invention proposes, this system comprises:

LSSVM model is set up unit: using 1100 groups of initial samples as training sample, be designated as x wherein _i∈ R ^prepresent i group input sample, corresponding to generator power, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening, y _i∈ R is i group output sample, and corresponding to the content of smoke components NOx, p is input variable number, and n is initial training sample size, and utilizes initial sample to build LSSVM model, realizes the prediction to NOx content;

Smoke components content prediction unit: the sample x to new sampling _qprediction, obtains predicted value

Sample predicated error computing unit: calculate sample (x according to formula _q, y _q) predicated error Er;

Predicated error judging unit: judgement predicated error: if Er> Δ enters updating block, set Δ=0.07 here; Δ is error threshold,, otherwise enter test sample book judging unit;

Sample point is chosen unit recently: from historical data, choose apart from the new nearest sample point (x of sample _k, y _k);

Updating type determining unit: according to new sample (x _q, y _q) sample (x nearest with it _k, y _k) between distance || x _q-x _k|| ₂determine updating type;

Eigenmatrix updating block:: according to definite updating type, contrary by formula～formula incremental computations eigenmatrix H;

Soft-sensing model updating block: by the new H trying to achieve ^-1substitution formula, obtains corresponding model parameter α and b, and model is upgraded.

In order to verify the prediction effect of the soft-sensing model of LSSVM and online updating, also set up the LSSVM model of not implementing to upgrade simultaneously and contrasted, adopt root error (R _mSE) and average relative error (M _rE) as the evaluation index of forecast result of model: $R_{\mathrm{MSE}}=\sqrt{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(y_i-{\hat{y}}_i)}^2/N},M_{\mathrm{RE}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\left|\frac{y_i-{\hat{y}}_i}{y_i}\right|\×100\%.$ Before and after LSSVM model modification, predicting the outcome of test sample book contrasted in Table 1.

Table 1

As shown in table 1, adopt the performance of the soft-sensing model after LSSVM upgrades to compare and have greatly improved with the model not upgrading, precision of prediction is improved.Fig. 4 is that the LSSVM model that utilizes the present invention and do not add renewal carries out to NOx discharge in boiler smoke the comparison diagram that predicts the outcome that modeling obtains.Wherein, first 1100 groups is initial training sample, and latter 270 groups is test sample book.As seen from the figure, when emission performance changes, the LSSVM model that does not add renewal increases gradually to the predicated error of NOx, and because the present invention has applied online updating, can keep higher precision of prediction always.

The present invention also provides a kind of model, and this model obtains according to the above-described flue gas in power station boiler flexible measurement method modeling based on LSSVM and online updating.The present invention proposes the soft measuring system of flue gas in power station boiler based on LSSVM and online updating, reduced the computation complexity of model, be conducive to Project Realization, the safety of station boiler and optimization operation are had great significance.

Above-mentioned example is used for illustrating the present invention, rather than is limited.In claim protection domain of the present invention, any to modification of the present invention is all fallen within the scope of protection of the present invention.

Claims (7)

1. the soft measuring system of the flue gas in power station boiler based on LSSVM and online updating, it is characterized in that, described soft measuring system comprises model prediction subsystem, Performance Detection subsystem and model modification subsystem, described model prediction subsystem is connected to performance prediction subsystem, described performance prediction subsystem is connected to model modification subsystem, described model modification subsystem feeds back to model prediction subsystem, and wherein model prediction subsystem comprises that LSSVM model sets up unit and smoke components content prediction unit; Performance Detection subsystem comprises sample predicated error computing unit and predicated error judging unit; Model modification subsystem comprises that nearest sample point chooses unit, updating type identifying unit, eigenmatrix updating block, soft-sensing model updating block.

2. soft measuring system according to claim 1, is characterized in that,

Described LSSVM model is set up unit, for collecting initial training sample, builds LSSVM model;

Described smoke components content prediction unit, utilizes this model to predict smoke components content;

Described sample predicated error computing unit, for the measurement value sensor y of the smoke components content when actual _qafter collecting, calculate sample (x _q, y _q) predicated error Er;

Described predicated error judging unit, for judging predicated error: if Er> Δ, Δ is error threshold, enters nearest sample point and chooses unit; Otherwise need to judge whether test sample book finishes, if finish system end of run, otherwise enter smoke components content prediction unit;

Described nearest sample point is chosen unit, for choosing from history data apart from new sample (x _q, y _q) nearest sample point (x _k, y _k), wherein

Described updating type identifying unit, according to new sample (x _q, y _q) sample (x nearest with it _k, y _k) between distance judge and definite updating type;

Described eigenmatrix updating block, according to definite updating type, the initial LSSVM model of LSSVM model being set up to unit acquisition carries out incremental update, to eigenmatrix H ^-1calculating upgrade;

Described soft-sensing model updating block, for by the eigenmatrix H of the new model of trying to achieve ^-1calculate corresponding model parameter α and b, realize the renewal to flue gas soft-sensing model h (x), recycle new model smoke components content is predicted.

3. soft measuring system according to claim 2, it is characterized in that, build LSSVM model specifically: by sensor measurement generator power, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening signal, and measured value is deposited in DCS historical data base; Select above-mentioned measured value as the input variable of soft-sensing model, the smoke components content that predict, as the output variable of model, is chosen some sections of large and representative operating modes of coverage as initial training sample from history data storehouse, is designated as

x wherein _i∈ R ^prepresent i group input sample, corresponding to the generator power of measuring, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening, y _i∈ R is i group output sample, and corresponding to the content of smoke components, p is input variable number, and n is sample size, and builds LSSVM model.

4. soft measuring system according to claim 2, it is characterized in that, smoke components content is predicted specifically: the generator power that sensor is newly recorded, each coal pulverizer coal-supplying amount, each coal pulverizer inlet primary air flow, each layer of Secondary Air and after-flame wind throttle opening data are as input variable x _q, the soft measurement LSSVM model formation of the flue gas content that substitution is initial:

obtain the soft measured value of smoke components content

its Kernel Function is chosen for Gaussian radial basis function

σ is kernel functional parameter, and h (x) is the predicted value of smoke components content.

5. soft measuring system according to claim 4, is characterized in that, being wherein calculated as follows of predicated error Er: $\mathrm{Er}=\left|\frac{{\hat{y}}_q-y_q}{y_q}\right|\×100\%.$

6. soft measuring system according to claim 2, is characterized in that, the judgment criterion adopting in described updating type identifying unit is:

(i) if || x _k-x _q|| ₂> δ ₁, model is implemented to sample and appends renewal, directly by new sample (x _q, y _q) join in previous historical data base;

(ii) if || x _k-x _q|| ₂≤ δ ₁, model is implemented to sample and replace renewal, use new sample (x _q, y _q) replace in previous historical data base and satisfy condition || x _k-x _q|| ₂≤ δ ₂similar sample;

δ wherein ₁by the mean distance between historical training data sample, determined δ ₂be made as 0.5 δ ₁.

7. soft measuring system according to claim 2, is characterized in that, the update strategy of described eigenmatrix updating block is divided into sample increase and sample is deleted two kinds of patterns: if implement sample, append renewal, directly carry out sample increase; If implement sample, replace renewal, first carry out sample and delete, and then carry out sample increase.

Images