The Research And Application Of Bayesian Method In Chemical Soft-sensor

Bayesian Learning Theory represents various knowledge and uncertainty with probability. The learning and inference are realized by probabilistic rules. Therefore, it is a strong tool dealing with uncertain information. This thesis mainly studies the basic point, application of Bayesian Learning Theory. The application of data classification and the soft sensor model based on Bayesian method are mainly studied as the key problem.This dissertation concentrated on the research work listed below and achieved some creative results.(1) Constructing sub-models can increase estimation accuracy in soft sensing modeling, and the construction of multi-model is based on the classification of the original data set. Among the methods of data classification, Naive Bayesian classifier has been widely applied because of its simplicity and efficiency. The continuous class variables are firstly divided into several categories, then the "3σ" rule based on probability theory is proposed to discretize the attributes. In order to eliminate the interferences from the training sample, the optimal sub sample set is selected from the training sample set by genetic algorithm. Finally the preprocessed training sample is used to build the Bayesian classifier. Both UCI data sets and the on-line monitoring data sets from the process of production for Bisphenol-A (BPA) are made experiment, and the simulation results show that it is possible to reliably improve the naive Bayesian classifier by using data discretization and selected as part of data pre-processing.(2) A new approach based on Bayesian network applied to chemical soft sensor is proposed. The network model is based on knowledge of the field experts and the mechanism of process, and a weighted combination of several normal distribution functions is used to approximate the joint probability distribution in Bayesian network, and then the estimated formula for Bayesian network is been given. The parameters of the model are estimated by processing real time data from a productive plant for Bisphenol A, and the model based on Bayesian network shows good results. Compared with support vector machine, the Bayesian network saves a lot of the estimated process parameters and has considerable accuracy. It is an effective method for soft sensor modeling.(3) In order to improve the estimation accuracy of the soft sensor model, a new nonlinear multi-modeling method based on Bayesian classify algorithm and relevance vector machine is proposed in the paper. The algorithm classifies the inputs by Bayesian classifier, and then trains each class by different relevance vector regression machines, and obtains the final result by the"Switch"way. The proposed algorithm is used for a soft sensor model for the bisphenol-A productive process. The experimental results indicate the proposed algorithm is superior compared with the single model of SVM and has certain application value.