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    Learning of Interval and General Type-2 Fuzzy Logic Systems using Simulated Annealing: Theory and Practice

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    SA-T2FLS_REVIEW.pdf (346.0Kb)
    Date
    2016-04-01
    Author
    Almaraashi, M.;
    John, Robert, 1955-;
    Hopgood, A.;
    Ahmadi, S.
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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.
    Description
    Citation : Almaraashi, M. John, R., Hopgood, A. and Ahmadi, S. (2016) Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice. Information Sciences, 360, pp. 21-42
    URI
    http://hdl.handle.net/2086/12236
    DOI
    http://dx.doi.org/10.1016/j.ins.2016.03.047
    ISSN : 0020-0255
    Research Group : Centre for Computational Intelligence
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2966]

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