Complexity management methodology for fuzzy systems with feedback rule bases

  • Authors:

  • Alexander Gegov;David Sanders;Boriana Vatchova

  • Affiliations:

  • School of Computing, University of Portsmouth, Buckingham Building, Portsmouth, UK;School of Engineering, University of Portsmouth, Angleasea Building, Portsmouth, UK;Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 2014

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Abstract

This paper proposes a complexity management methodology for fuzzy systems with feedback rule bases. The methodology is based on formal methods for presentation, manipulation and transformation of fuzzy rule bases. First, Boolean matrices are used for formal presentation of rule bases. Then, binary merging operations are used for formal manipulation of rule bases. Finally, repetitive merging operations are used for formal transformation of rule bases. The formal methods facilitate the understanding and modelling of fuzzy systems in terms of interacting subsystems. In particular, the methods reduce the qualitative complexity in fuzzy systems by improving the transparency of the rule bases.