A multi model composition framework based on bayesian model comparision and its application in soft sensor modeling

In order to improve the prediction performance of single model based soft sensor, the features of the current model combination frameworksby analynizing, a new multi model combination framework based on the bayesian model comparison is proposed. In this framework, fuzzy c means clustering to the historial data is used to analyze the production states, then the prediction performance of sub models at different states are compared based on bayesian model comparison. The comparing results are the basis of the model combination stratery at different states. With adapting cross validation predictive distribution, the samples got from the trained models are used to successfully reduce computation load of model comparion.The framework has obtained good results in the practical application.