Title :
A Neural Network-Based On-line Monitoring Model of Process Mean and Variance Shifts
Author :
Wu, Bin ; Yu, Jian-Bo
Author_Institution :
Res. Center of Service Sci., Shanghai Dianji Univ., Shanghai, China
Abstract :
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.
Keywords :
artificial intelligence; computerised monitoring; neural nets; mean shift; neural network based identification model; neural network based online monitoring model; selective network ensemble approach; variance shift; Artificial neural networks; Classification algorithms; Correlation; Monitoring; Neurons; Process control; Training; correlated manufacturing process; on-line monitoring; selective neural network ensemble; statistical process control;
Conference_Titel :
E-Business and E-Government (ICEE), 2010 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3997-3
DOI :
10.1109/ICEE.2010.661
