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

Almaraashia, M., John, Robert, 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

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/818363
Keywords: Simulated annealing; Interval type-2 fuzzy logic systems; General type-2 fuzzy logic systems; Learning
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.ins.2016.03.047
Depositing User: John, Professor Robert
Date Deposited: 04 Apr 2016 08:06
Last Modified: 04 May 2020 18:12
URI: https://eprints.nottingham.ac.uk/id/eprint/32599

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