A new indirect approach to the type-2 fuzzy systems modeling and design

  • Authors:

  • M. H. Fazel Zarandi;A. Doostparast Torshizi;I. B. Turksen;B. Rezaee

  • Affiliations:

  • Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran and Knowledge Intelligent Systems Laboratory, University of Toronto, Toronto, Canada;Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran;Knowledge Intelligent Systems Laboratory, University of Toronto, Toronto, Canada and Department of Industrial Engineering, TOBB University of Economics and Technology, Sogutozu, Ankara, Turkey;Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

This paper proposes a new method for designing Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) considering two issues: first, quality of clustering the output space and secondly, approximating the output of the IT2 FLS based on a new output processing method. Based on these two issues, we present a new cluster validity index capable of being used for type-1 Fuzzy C-Means (FCMs), Interval Type-2 FCM (IT2 FCM), and Possibilistic C-Means (PCMs) clustering algorithms. This validity index is highly efficient in determining clusters with the least similarity between them and the highest similarity between data vectors in each cluster. Then, a new definition for uncertainty bounds is presented in order to eliminate the type-reduction process in IT2 FLSs and to increase accuracy of the existing uncertainty bounds in the literature. Finally, effectiveness of the proposed approaches compared to several well-known existing methods has been investigated. Computational results have verified accuracy and effectiveness of the proposed method.