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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wu, P., Yang, KJ., Hung, YY. (2005). The Study of Electromagnetism-Like Mechanism Based Fuzzy Neural Network for Learning Fuzzy If-Then Rules. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_53
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DOI: https://doi.org/10.1007/11554028_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
Online ISBN: 978-3-540-31997-9
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