A type-2 fuzzy c-regression clustering algorithm for Takagi–Sugeno system identification and its application in the steel industry

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...

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Bibliographic Details
Published in:	Information sciences Vol. 187; pp. 179 - 203
Main Authors:	Fazel Zarandi, M.H., Gamasaee, R., Turksen, I.B.
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 15.03.2012
Subjects:	Algorithms Clustering Fuzzy Fuzzy logic Fuzzy set theory Gaussian mixture Intervals Iron and steel industry IT2F c-regression clustering Mathematical models Multiple-regression Steel industry Structure identification Weighted least square Structure identification Gaussian mixture Multiple-regression IT2F c-regression clustering Steel industry Weighted least square
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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