Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice
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 maximi...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/32599/ |
| _version_ | 1848794446113538048 |
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| author | Almaraashia, M. John, Robert Hopgood, A. Ahmadi, S. |
| author_facet | Almaraashia, M. John, Robert Hopgood, A. Ahmadi, S. |
| author_sort | Almaraashia, M. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:16:19Z |
| format | Article |
| id | nottingham-32599 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:16:19Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-325992020-05-04T18:12:47Z https://eprints.nottingham.ac.uk/32599/ Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice Almaraashia, M. John, Robert Hopgood, A. Ahmadi, S. 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. Elsevier 2016-09-10 Article PeerReviewed 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 Simulated annealing; Interval type-2 fuzzy logic systems; General type-2 fuzzy logic systems; Learning http://www.sciencedirect.com/science/article/pii/S0020025516302225 doi:10.1016/j.ins.2016.03.047 doi:10.1016/j.ins.2016.03.047 |
| spellingShingle | Simulated annealing; Interval type-2 fuzzy logic systems; General type-2 fuzzy logic systems; Learning Almaraashia, M. John, Robert Hopgood, A. Ahmadi, S. Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| title | Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| title_full | Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| title_fullStr | Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| title_full_unstemmed | Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| title_short | Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| title_sort | learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice |
| topic | Simulated annealing; Interval type-2 fuzzy logic systems; General type-2 fuzzy logic systems; Learning |
| url | https://eprints.nottingham.ac.uk/32599/ https://eprints.nottingham.ac.uk/32599/ https://eprints.nottingham.ac.uk/32599/ |