Learning of Interval and General Type-2 Fuzzy Logic Systems using Simulated Annealing: Theory and Practice

Date

2016-04-01

Advisors

Journal Title

Journal ISSN

ISSN

0020-0255

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

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 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.

Description

Keywords

simulated annealing, interval type-2 fuzzy logic systems, general type-2 fuzzy logic systems, learning

Citation

Almaraashi, M. John, R., 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

Rights

Research Institute