Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice
- Submitting institution
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University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11093770
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2016.03.047
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 21
- Volume
- 360
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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B - Computational Intelligence
- Citation count
- 28
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Type-2 fuzzy logic treats memberships as fuzzy variables, to overcome the precise nature of type-1 sets. This research circumvents the increased complexity through its innovative use of simulated annealing for tuning the parameters in both interval type-2 (IT2) and the more sophisticated general type-2 (GT2). Jerry M. Mendel, pioneer of type-2, states “The paper is important because it is one of the first to illustrate the use of GT2 fuzzy systems. It is very well written and shows a comparison with IT2 fuzzy systems very clearly, and that a GT2 fuzzy system can outperform an IT2 fuzzy system.” mendel@sipi.usc.edu
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
