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

Almaraashia, M.; John, Robert; Hopgood, A.; Ahmadi, S.

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Authors

M. Almaraashia

Robert John

A. Hopgood

S. Ahmadi



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 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.

Citation

Almaraashia, M., John, R., Hopgood, A., & Ahmadi, S. (2016). Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice. Information Sciences, 360, https://doi.org/10.1016/j.ins.2016.03.047

Journal Article Type Article
Acceptance Date Mar 28, 2016
Online Publication Date Apr 1, 2016
Publication Date Sep 10, 2016
Deposit Date Apr 4, 2016
Publicly Available Date Apr 4, 2016
Journal Information Sciences
Print ISSN 0020-0255
Electronic ISSN 1872-6291
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 360
DOI https://doi.org/10.1016/j.ins.2016.03.047
Keywords Simulated annealing; Interval type-2 fuzzy logic systems; General type-2 fuzzy logic systems; Learning
Public URL https://nottingham-repository.worktribe.com/output/818363
Publisher URL http://www.sciencedirect.com/science/article/pii/S0020025516302225
Contract Date Apr 4, 2016

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