Fuzzy Modeling Via On-Line Clustering and Support Vector Machine

Abstract. This paper describes a novel fuzzy rule-based modeling approach for some slow industrial processses. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.

• Type: Conference
• Year: 2007
• Where: ICIC
• Authors: Julio César Tovar, Wen Yu, Xiaoou Li
• Keywords: Fuzzy Neural Networks, Fuzzy Rule-based Modeling, ICIC 2007, Slow Industrial Processses