A Neural Network-Based On-line Monitoring Model of Process Mean and Variance Shifts

In this paper, a neural network-based identification model is proposed for both mean and variance shifts in correlated processes. The proposed model uses a selective network ensemble approach named DPSOEN to obtain the improved generalization performance. The model is capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both mean and variance shifts in one time.