Reference : Learning of interval and general type-2 fuzzy logic systems using simulated annealing...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/196366
Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice
English
Almaraashi, Majid [> >]
John, Robert [> >]
Hopgood, Adrian mailto [Université de Liège > > HEC - Ecole de gestion de l'ULG : Direction générale >]
Ahmadi, Samad [> >]
2016
Information Sciences
Elsevier
360
Information Sciences (2016)
21–42
Yes (verified by ORBi)
International
0020-0255
1872-6291
[en] Simulated annealing ; Interval type-2 fuzzy logic systems ; General type-2 fuzzy logic systems ; Learning
[en] 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.
http://hdl.handle.net/2268/196366
10.1016/j.ins.2016.03.047
http://www.sciencedirect.com/science/article/pii/S0020025516302225

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