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http://dx.doi.org/10.5391/JKIIS.2005.15.2.204

Online Evolving TSK fuzzy identification  

Kim, Kyoung-Jung (연세대학교 전기전자공학과)
Park, Chang-Woo (전자부품연구원 정밀기기연구센터)
Kim Eun-Tai (연세대학교 전기전자공학과)
Park, Mignon (연세대학교 전기전자공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.2, 2005 , pp. 204-210 More about this Journal
Abstract
This paper presents online identification algorithm for TSK fuzzy model. The proposed algorithm identify structure of premise part by using distance, and obtain the parameters of the piecewise linear function consisting consequent part by using recursive least square. Only input space was considered in Most researches on structure identification, but input and output space is considered in the proposed algorithm. By doing so, outliers are excluded in clustering effectively. The existing other algorithm has disadvantage that it is sensitive to noise by using data itself as cluster centers. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. Model can be obtained through one pass and it is not needed to memorize many data in the proposed algorithm.
Keywords
online; evolving; fuzzy identification; self-organizing;
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