A TSK fuzzy inference algorithm for online identification

  • Authors:

  • Kyoungjung Kim;Eun Ju Whang;Chang-Woo Park;Euntai Kim;Mignon Park

  • Affiliations:

  • Department of electrical and electronic engineering, Yonsei University, Seoul, Korea;Department of electrical and electronic engineering, Yonsei University, Seoul, Korea;Korea electronics technology institute, Buchon-Si, Kyunggi-Do, Korea;Department of electrical and electronic engineering, Yonsei University, Seoul, Korea;Department of electrical and electronic engineering, Yonsei University, Seoul, Korea

  • Venue:
  • FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
  • Year:
  • 2005

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Abstract

This paper proposes an online self-organizing identification algorithm for TSK fuzzy model. The structure of TSK fuzzy model is identified using distance. Parameters of the piecewise linear function consisting consequent part are obtained using recursive version of combined learning method of global and local learning. Both input and output spaces are considered in the proposed algorithm to identify the structure of the TSK fuzzy model. By processing clustering both in input and output space, outliers are excluded in clustering effectively. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. The proposed algorithm can obtain a TSK fuzzy model through one pass. By using the proposed combined learning method, the estimated function can have high accuracy.