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Self-Tuning for Fuzzy Rule Generation Based upon Fuzzy Singleton-type Reasoning Method
Yan Shi* and Masaharu Mizumoto**
*School of Engineering, Kyushu Tokai University 9-1-1, Toroku, Kumamoto 862-8652, Japan
**Division of Information and Computer Sciences Osaka Electro-Communication University Neyagawa, Osaka 572-8530, Japan
Received:March 16, 1998Accepted:November 19, 1998Published:June 20, 1999
Keywords:Fuzzy singleton-type reasoning method, Fuzzy rule generation, Neuro-fuzzy learning algorithm, Fuzzy cmeans clustering algorithm
Abstract
Using fuzzy singleton-type reasoning method, we propose a self-tuning method for fuzzy rule generation. We give a neurofuzzy learning algorithm for tuning fuzzy rules under fuzzy singleton-type reasoning method, then roughly design initial tuning parameters of fuzzy rules based on a fuzzy clustering algorithm before learning a fuzzy model. This should reduce learning time and fuzzy rules generated by our approach are reasonable and suitable for the identified model. We demonstrate our proposal’s efficiency by identifying nonlinear functions.
Cite this article as:Y. Shi and M. Mizumoto, “Self-Tuning for Fuzzy Rule Generation Based upon Fuzzy Singleton-type Reasoning Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.3 No.3, pp. 200-206, 1999.Data files:
