Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice
Almaraashia, M. and John, Robert and 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. ISSN 1872-6291
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 gen- eral 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 bench- mark 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 sys- tems, 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.
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