A TSK fuzzy inference algorithm for online identification

Kyoungjung Kim, Eun Ju Whang, Chang Woo Park, Euntai Kim, Mignon Park

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)179-188
Number of pages10
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Issue numberPART I
Publication statusPublished - 2005
EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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