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

Majid Almaraashi, Robert John, Adrian Hopgood, Samad Ahmadi

    Research output: Contribution to journalArticlepeer-review

    304 Downloads (Pure)

    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 Sep 2016

    Keywords

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

    Fingerprint

    Dive into the research topics of 'Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice'. Together they form a unique fingerprint.

    Cite this