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Title: A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry
Authors: Zarandi, Mohammad Hossein Fazel
Gamasaee, R.
Türkşen, İsmail Burhan
Keywords: IT2F c-regression clustering
Structure identification
Gaussian mixture
Weighted least square
Steel industry
Issue Date: 2012
Publisher: Elsevier Science Inc
Abstract: This paper proposes a new type-2 fuzzy c-regression clustering algorithm for the structure identification phase of Takagi-Sugeno (T-S) systems. We present uncertainties with fuzzifier parameter "m'. In order to identify the parameters of interval type-2 fuzzy sets, two fuzzifiers 'm(1)" and "m(2)" are used. Then, by utilizing these two fuzzifiers in a fuzzy c-regression clustering algorithm, the interval type-2 fuzzy membership functions are generated. The proposed model in this paper is an extended version of a type-1 FCRM algorithm [25], which is extended to an interval type-2 fuzzy model. The Gaussian Mixture model is used to create the partition matrix of the fuzzy c-regression clustering algorithm. Finally, in order to validate the proposed model, several numerical examples are presented. The model is tested on a real data set from a steel company in Canada. Our computational results show that our model is more effective for robustness and error reduction than type-1 NFCRM and the multiple-regression. (C) 2011 Elsevier Inc. All rights reserved.
ISSN: 0020-0255
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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