Tuning Fuzzy Rules Using Fuzzy Singleton-Type Reasoning Method

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  • SHI Yan
    School of Engineering, Kyushu Tokai University
  • MIZUMOTO Masahru
    Division of Information and Computer Sciences, Osaka Electro-Communication University

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  • ファジィシングルトン型推論法を用いたファジィ規則のチューニング
  • ファジィ シングル トンガタ スイロンホウ オ モチイタ ファジィ キソク ノ

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Using fuzzy clustering and neuro-fuzzy techniques, was develop a new learning approach for tuning fuzzy rules under fuzzy singleton-type reasoning method. In this approach, using training data, we roughly design initial fuzzy rules based on fuzzy clustering algorithms before the learning of a fuzzy model, so that the fuzzy rules generated by a neuro-fuzzy learning algorithm are more reasonable and suitable for identifying the system model than the conventional method. Moreover, we show the efficiency of the developed approach by means of numerical examples.

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