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

  • Authors:

  • M. H. Fazel Zarandi;R. Gamasaee;I. B. Turksen

  • Affiliations:

  • Department of Industrial Engineering, Amirkabir University of Technology, P.O. BOX 15875-4413, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology, P.O. BOX 15875-4413, Tehran, Iran;Department of Industrial Engineering, TOBB University of Economics and Technology, Sogutozu, Ankara, Turkey and Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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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.