The study of electromagnetism-like mechanism based fuzzy neural network for learning fuzzy if-then rules

Authors:
Peitsang Wu;Kung-Jiuan Yang;Yung-Yao Hung
Affiliations:
Department of Industrial Engineering and Management, I-Shou University, Taiwan, R.O.C.;Department of Information Management, Fortune Institute of Technology, Taiwan, R.O.C.;Department of Industrial Engineering and Management, I-Shou University, Taiwan, R.O.C.
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

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Abstract

In this paper, a meta-heuristic algorithm (electromagnetism-like mechanism, EM) for fuzzy neural network training is introduced. Electromagnetism-like mechanism simulates the electromagnetism theory of physics by considering each sample point to be an electrical charge. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. Besides, the electromagnetism-like mechanism is not easily falling into local optimum. Therefore, the purpose of this study is to use the electromagnetism-like mechanism to develop the fuzzy neural networks (EMFNN), and employ this EMFNN to train fuzzy if-then rules. According to the case, the EMFNN could successfully generalize new fuzzy if-then rules.