A Gaussian process ensemble modeling method based on boosting algorithm

In order to improve the estimation accuracy of a soft sensor in the chemical process, an ensemble model is proposed based on Boosting and Gaussian process algorithms. Using Gaussian process as a base learner, a leveraging learner is constructed by Boosting algorithm. The ensemble model is obtained by dynamically averaging the regression functions trained by leveraging learners. Finally, the algorithm is applied to a soft sensor model for a production plant of Bisphenol A. Simulation results show that the integration algorithm has higher accuracy and generalization ability comparing to a single Gaussian process model.