Gaussian process ensemble soft sensor modeling based on improved Bagging algorithm
In order to improve the accuracy and generalization ability of soft sensor for complex industrial process, a Gaussian process ensemble soft sensor modeling algorithm based on the improved bagging algorithm is proposed. This algorithm uses Gaussian process regression algorithm to build base learners and the resample method of bagging algorithm to form training subsets of base learners. A criteria for feature ordering base on normalized mutual information is proposed with selecting input features of base learners, which can implement supervised feature perturbance in the ensemble modeling for the sake of improving the diversity between base learners. When estimating the output of the test sample according to the output variances given by Gaussian process base learners, several base learners are selected adaptively to calculate the output of ensemble model. A soft sensor modeling simulation using the data from the reactors of industrial Bisphenol A production units shows the effectiveness of the algorithm.