Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice

Majid Almaraashi, Robert John, Adrian Hopgood, Samad Ahmadi

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    Abstract

    This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four benchmark problems including real-world problems. The type-2 fuzzy logic system models are compared in their ability to model uncertainties associated with these problems. Issues related to this combination between simulated annealing and fuzzy logic systems, including type-2 fuzzy logic systems, are discussed. The results demonstrate that learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can add more capability to modeling than interval type-2 fuzzy logic systems. This finding can be seen as an important advance in type-2 fuzzy logic systems research and should increase the level of interest in the modeling applications of general type-2 fuzzy logic systems, despite their greater computational load.

    Original languageEnglish
    Pages (from-to)21-42
    Number of pages22
    JournalInformation Sciences
    Volume360
    Early online date1 Apr 2016
    DOIs
    Publication statusPublished - 10 Sept 2016

    Keywords

    • simulated annealing
    • Interval type-2 fuzzy logic systems
    • general type-2 fuzzy logic systems
    • learning

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