Type reduction operators for interval type–2 defuzzification

Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set. Type–2 defuzzification maps type–2 fuzzy sets to non–fuzzy values. Type reduction maps type–2 fuzzy...

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Main Authors: Runkler, Thomas A., Chen, Chao, John, Robert
Format: Article
Language:English
Published: Elsevier 2018
Online Access:https://eprints.nottingham.ac.uk/54958/
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author Runkler, Thomas A.
Chen, Chao
John, Robert
author_facet Runkler, Thomas A.
Chen, Chao
John, Robert
author_sort Runkler, Thomas A.
building Nottingham Research Data Repository
collection Online Access
description Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set. Type–2 defuzzification maps type–2 fuzzy sets to non–fuzzy values. Type reduction maps type–2 fuzzy sets to type–1 fuzzy sets, in order to make type–2 defuzzification easier and to implement more efficient type–2 defuzzification algorithms. This paper is a first step towards a theoretical foundation of the emerging field of type reduction. Five mathematical properties of type reduction are defined, and two existing type reduction methods (Nie–Tan and uncertainty weight) are examined with respect to our five properties. Furthermore, two new type reduction methods are proposed: consistent linear type reduction and consistent quadratic type reduction. All our five properties are satisfied by consistent quadratic type reduction.
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spelling nottingham-549582019-08-10T04:30:17Z https://eprints.nottingham.ac.uk/54958/ Type reduction operators for interval type–2 defuzzification Runkler, Thomas A. Chen, Chao John, Robert Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set. Type–2 defuzzification maps type–2 fuzzy sets to non–fuzzy values. Type reduction maps type–2 fuzzy sets to type–1 fuzzy sets, in order to make type–2 defuzzification easier and to implement more efficient type–2 defuzzification algorithms. This paper is a first step towards a theoretical foundation of the emerging field of type reduction. Five mathematical properties of type reduction are defined, and two existing type reduction methods (Nie–Tan and uncertainty weight) are examined with respect to our five properties. Furthermore, two new type reduction methods are proposed: consistent linear type reduction and consistent quadratic type reduction. All our five properties are satisfied by consistent quadratic type reduction. Elsevier 2018-10-31 Article PeerReviewed application/pdf en cc_by_nc_nd https://eprints.nottingham.ac.uk/54958/1/Defuzzification%20manuscript%20%28002%29.pdf Runkler, Thomas A., Chen, Chao and John, Robert (2018) Type reduction operators for interval type–2 defuzzification. Information Sciences, 467 . pp. 464-476. ISSN 1872-6291 http://dx.doi.org/10.1016/j.ins.2018.08.023 doi:10.1016/j.ins.2018.08.023 doi:10.1016/j.ins.2018.08.023
spellingShingle Runkler, Thomas A.
Chen, Chao
John, Robert
Type reduction operators for interval type–2 defuzzification
title Type reduction operators for interval type–2 defuzzification
title_full Type reduction operators for interval type–2 defuzzification
title_fullStr Type reduction operators for interval type–2 defuzzification
title_full_unstemmed Type reduction operators for interval type–2 defuzzification
title_short Type reduction operators for interval type–2 defuzzification
title_sort type reduction operators for interval type–2 defuzzification
url https://eprints.nottingham.ac.uk/54958/
https://eprints.nottingham.ac.uk/54958/
https://eprints.nottingham.ac.uk/54958/