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. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Wu, P., Yang, K. J., & Hung, Y. Y. (2005). The study of electromagnetism-like mechanism based fuzzy neural network for learning fuzzy if-then rules. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3684 LNAI, pp. 382–388). Springer Verlag. https://doi.org/10.1007/11554028_53
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