WO2016101182A1 - Procédé de prévision d'indicateur de type d'intervalle basé sur un réseau bayésien et machine d'apprentissage extrême - Google Patents

Procédé de prévision d'indicateur de type d'intervalle basé sur un réseau bayésien et machine d'apprentissage extrême Download PDF

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WO2016101182A1
WO2016101182A1 PCT/CN2014/094839 CN2014094839W WO2016101182A1 WO 2016101182 A1 WO2016101182 A1 WO 2016101182A1 CN 2014094839 W CN2014094839 W CN 2014094839W WO 2016101182 A1 WO2016101182 A1 WO 2016101182A1
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model
interval
output
prediction
hidden layer
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刘民
宁克锋
董明宇
吴澄
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清华大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing

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  • the invention belongs to the fields of automatic control, information technology and advanced manufacturing, and particularly relates to a Bayesian network and an extreme learning machine (ELM)-based interval type for a complex industrial production process in which it is difficult to establish a mechanism model and has a large amount of historical production data. Indicator forecasting method.
  • ELM extreme learning machine
  • Production index forecasting is one of the key technologies involved in the operation optimization and dynamic scheduling of production processes.
  • production data often contains various uncertainties, based on neural networks and support vectors.
  • the forecast value of the index given by the conventional predictive model and the actual measured value of the index often have large deviations, which affects the operation optimization and dynamic scheduling effect.
  • the use of interval-type index forecasting method is one of the effective ways to solve the above-mentioned index forecasting problem. .
  • the present invention is directed to a complex production process in which it is difficult to establish a mechanism model and has a large amount of historical production data, and proposes an interval type index prediction method based on Bayesian network and extreme learning machine (ELM).
  • ELM extreme learning machine
  • the invention aims at the uncertainty characteristics of complex production process, uses interval numbers to describe production indexes, utilizes actual operation data in complex production processes, uses asymmetric Gaussian distribution Bayesian and ELM methods to model interval indicators, and adopts The pair of mutually reciprocal weights are adaptively adjusted to obtain the upper boundary model and the lower boundary model as the forecast interval of the production index.
  • the above-mentioned interval type indicator forecasting method can predict the production index in the actual production process, and is used to guide the operation optimization and dynamic scheduling of the production process.
  • Step (1) Data acquisition and preprocessing
  • Data acquisition system is used to collect data from actual industrial production processes, and the above data is processed into Training data:
  • x i (x i,1 ,...,x i,n )
  • N is the number of training data samples
  • n is the dimension of the input variable
  • Step (2) Construct a double ELM model based on asymmetric Gaussian distribution Bayes
  • h(x) is the hidden layer node function of ELM
  • ⁇ is the output layer weight
  • ⁇ is the model error
  • the output of the ELM model can be assumed to be an asymmetric Gaussian distribution as follows:
  • b is the variance parameter of the asymmetric Gaussian distribution
  • w is the weight of the asymmetric Gaussian distribution
  • the likelihood function of the training data can be written as:
  • H 1 and t 1 are the hidden layer output matrix and the output vector of the sample set satisfying t ⁇ h ⁇ , respectively, and H 2 and t 2 are the hidden layer output matrix and the output vector of the sample set satisfying t ⁇ h ⁇ , respectively;
  • M is the number of hidden layer nodes, and a and ⁇ k are parameters of the Gaussian distribution;
  • Step (3) Initialization of a double ELM model based on asymmetric Gaussian distribution Bayesian
  • the number of selected input layer neural nodes is the same as the training sample dimension n, the number of output neural nodes is 1, and the number of hidden layer nodes of the single hidden layer limit learning machine is M;
  • the excitation function h(x,o l ,r l ) of the hidden layer node can adopt Gaussian function/Sigmoid function/sine function/triangle base function/Hard Limit function;
  • H 1,1 and t 1,1 are the hidden layer output matrix and output values corresponding to the training samples with ⁇ 0 , respectively
  • H 1,2 and t 1,2 are the hidden layers corresponding to the training samples with ⁇ >0 , respectively.
  • Step (5) Parameter learning of the Bayesian ELM model with weight w 2 :
  • H 2,1 and t 2,1 are the hidden layer output matrix and output values corresponding to the training samples of ⁇ 0, respectively, and H 2,2 and t 2,2 are the hidden layers corresponding to the training samples with ⁇ >0, respectively.
  • Step (6) Adaptive adjustment of weights (w 1 , w 2 )
  • Step (7) repeating step (4), step (5), and step (6) until CI err satisfies the stop condition;
  • Step (8) On the basis of the completion of the above-mentioned model parameter learning, the interval type index prediction is performed as follows, assuming that the input variable is x,
  • t 1 and t 2 are the lower bound and upper bound of the predicted value of the interval type indicator, respectively;
  • Figure 1 Block diagram of the algorithm for the interval-based indicator prediction method based on Bayesian network and extreme learning machine.
  • Fig. 2 is a graph showing the comparison between the model output and the actual output for the prediction of the molten steel temperature in the LF production process.
  • the abscissa is the sample number
  • the blue small dot on the ordinate is the actual molten steel temperature value
  • the green curve and the red curve are the predicted upper bound value and the predicted lower bound value of the prediction model, respectively.
  • Fig. 3 is a diagram showing the weight adaptive adjustment process and the corresponding prediction interval change diagram of the present invention for the prediction of the molten steel temperature in the LF production process.
  • the abscissa is the number of iterations of the model learning
  • the blue curve and the red curve in the ordinate are the adaptive adjustment processes of the weights of the upper bound model and the lower bound model respectively
  • the green curve is the corresponding predicted interval value in the adjustment process.
  • the first step refinery production data collection
  • Step 2 Conduct AB-TELM model training
  • the Bayesian ELM model of the weight w 1 in the AB-TELM model (hereinafter referred to as the upper bound model) and the Bayesian ELM model of the weight w 2 (hereinafter referred to as the lower bound model)
  • the parameter and the parameters in the weight adaptive algorithm are initialized; on the basis of the initialization, the upper bound model and the given w 1 and w 2 are given according to steps (4) and (5) in the specification respectively.
  • Parameter learning of the lower bound model using the method in the specification of the present invention, adaptively adjusting w 1 and w 2 according to step (6); repeating the parameter learning process of the upper bound model, the lower bound model, and the self of w 1 and w 2 Adapt to the adjustment process until the model converges.
  • the optimal hidden layer node's excitation function and hidden layer node number need to be determined by cross-validation method.
  • the third step using the AB-TELM model for interval index prediction
  • the data acquisition system is used to collect the actual industrial production data of the refining furnace site, and the data is processed into the input data required by the AB-TELM model according to the first step of processing the training data, and the test samples are obtained. 578, and then use the AB-TELM model parameters obtained in the second step to calculate the interval type index prediction value according to step (8).
  • Figure (2) is the prediction result of the model when the interval is 10 degrees.
  • the red curve represents the lower bound value of the temperature prediction
  • the green curve represents the upper bound value of the temperature prediction. It can be seen from Fig. 2 that in the prediction results of the AB-TELM model, the predicted values of the upper bound model are larger than the predicted values of the lower bound model, and most of the actual data are located in the prediction interval of the AB-TELM model, indicating The feasibility of the model.
  • Figure (3) is its corresponding weight adaptive adjustment process and its corresponding prediction interval change graph, in which the green curve is the change process of the prediction interval, and the blue curve is the adaptive adjustment process of the lower bound model weight w 1 , the red curve The adaptive adjustment process for the upper bound model weight w 2 . It can be seen from the diagram (3) that after setting the expected prediction interval to 10 degrees, the lower bound model weight w 1 and the upper bound model weight w 2 can be self-according to the error between the actual predicted interval value of the model and the expected prediction interval. Adapt to the adjustment, and after 10 steps of iteration, can achieve the desired prediction interval value.
  • Table 1 compares the simulation results of the proposed algorithm AB-TELM with the common ELM and the dual model based on the support vector machine (including the linear kernel TSVR-1 and the Gaussian kernel TSVR-g).
  • the performance index is the mean square error. (RMSE).
  • #Nodes is the number of hidden layer nodes of the ELM category model
  • C and ⁇ are the error penalty coefficients and insensitive coefficients of the TSVR category model. It can be seen from Table 1 that the test accuracy of AB-TELM is greatly improved compared with the ELM, TSVR-1, and TSVR-g models, indicating the effectiveness of the AB-TELM model proposed by the present invention.
  • the first step refinery production data collection
  • Step 2 Conduct AB-TELM model training
  • the Bayesian ELM model of the weight w 1 in the AB-TELM model (hereinafter referred to as the upper bound model) and the Bayesian ELM model of the weight w 2 (hereinafter referred to as the lower bound model)
  • the parameter and the parameters in the weight adaptive algorithm are initialized; on the basis of the initialization, the upper bound model and the given w 1 and w 2 are given according to steps (4) and (5) in the specification respectively.
  • Parameter learning of the lower bound model using the method in the specification of the present invention, adaptively adjusting w 1 and w 2 according to step (6); repeating the parameter learning process of the upper bound model, the lower bound model, and the self of w 1 and w 2 Adapt to the adjustment process until the model converges.
  • the optimal hidden layer node's excitation function and hidden layer node number need to be determined by cross-validation method.
  • the third step using the AB-TELM model for interval index prediction
  • the data acquisition system is used to collect the actual industrial production data of the CMP site, and the data is processed into the input data required by the AB-TELM model according to the first step of processing the training data, and the test sample is obtained. Then, using the AB-TELM model parameters obtained in the second step, the interval type index prediction value is calculated according to step (8).

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Abstract

L'invention concerne un procédé de prévision d'indicateur de type d'intervalle basé sur un réseau bayésien et une machine d'apprentissage extrême, qui se rapportent aux domaines de la commande automatique, des technologies de l'information et de la fabrication avancée, et concernent, en particulier, l'apprentissage de paramètres d'un modèle ELM bayésien de distribution gaussienne asymétrique et le réglage adaptatif de poids asymétriques. Le procédé est caractérisé en ce qu'il comprend les étapes suivantes consistant : quant à la caractéristique de l'incertitude d'un processus de production complexe, à décrire des indicateurs de production par utilisation de nombres d'intervalles ; à utiliser une distribution gaussienne asymétrique comme distribution de sortie dans un modèle ELM, et à acquérir le modèle ELM bayésien ayant les poids ; et à apprendre des paramètres du modèle ELM bayésien sous une trame bayésienne d'expérience par utilisation de données d'exécution réelles dans le processus de production complexe ; sur la base, à apprendre une paire de poids réciproques par utilisation d'un procédé de réglage adaptatif ; et enfin, à acquérir une valeur de prévision des indicateurs de type d'intervalle. Au moyen du procédé de prévision d'indicateur de type d'intervalle, des indicateurs de production dans le processus de production pratique peuvent être prévus, et le procédé de prévision d'indicateur de type d'intervalle peut être utilisé pour guider une optimisation et une planification dynamique d'opération dans le processus de production.
PCT/CN2014/094839 2014-12-23 2014-12-24 Procédé de prévision d'indicateur de type d'intervalle basé sur un réseau bayésien et machine d'apprentissage extrême WO2016101182A1 (fr)

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