Multi-model quality prediction approach using fuzzy C-means clustering and support vector regression. (August 2017)

• Record Type: Journal Article
• Authors: Zhang, Min; Cai, Zhenyu; Cheng, Wenming
• Is Part Of: Advances in mechanical engineering. Volume 9:Number 8(2017:Aug.)
• Publication Date: 2017-08
• Subjects: Multi-model -- fuzzy C-means clustering -- support vector regression -- quality prediction -- adaptive mutation particle swarm optimization; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814017718474
• Languages: English
• ISSNs: 1687-8132

Abstract

Quality prediction of complex production process has increasingly attracted the interests of manufacturers and researchers. Complex production process has the characteristics of sub-process mutual coupling, data show nonlinear, multi-inputs and multi-outputs, and it is difficult to realize process quality prediction effectively. To solve this problem, a multi-model modeling approach based on fuzzy C-means clustering and support vector regression is proposed in this article. First, classify the operation conditions using fuzzy C-means clustering algorithm, then establish the local quality prediction models of multiple operation conditions using support vector regression, obtain multi-model with model weights using adaptive mutation particle swarm optimization, and implement the quality prediction of complex production process. This method solves the problems of nonlinear, wide operating condition range and prediction difficult. A case study of the Tennessee Eastman process shows that the proposed model is feasible and efficient.