Fuzzy neural modeling via clustering and support vector machines

Abstract

This paper describes a novel fuzzy rule-based modeling approach for some industrial processes. 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.