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

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

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