In order to improve the estimation accuracy of a soft sensor in the process of chemical production

a combination model for soft sensor is presented based on Gaussian process and Bayesian committee machine.The original data are classified into several subclasses

and then

the sub-models are built by Gaussian process regression.In order to get a global probabilistic prediction

Bayesian committee machine is used to combine the outputs of the sub-estimators.Finally

the algorithm is applied to a soft sensor model for a production plant of bisphenol A.Simulation results show that the integration algorithm can make full use of sample information in the actual production

and the estimated accuracy of model is improved

and the generalization ability is better

comparing to the traditional switch or a weighted combination of multiple model.