Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs

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dc.contributor.author Kouatli, Issam
dc.date.accessioned 2016-05-06T11:23:21Z
dc.date.available 2016-05-06T11:23:21Z
dc.date.copyright 2008 en_US
dc.identifier.issn 0368-492X en_US
dc.identifier.uri http://hdl.handle.net/10725/3694
dc.description.abstract Purpose – This paper seeks to identify and propose a standard approach for the selection and optimization of fuzzy sets used in fuzzy decision‐making systems. Design/methodology/approach – The design was based on two principles: selection and optimization. The selection methodology was based on the “Fuzzimetric Arcs” principle, which is an analogy of the trigonometric circle principle. This would allow an initial sinusoidal fuzzy set shape. Other shapes may also be selected using the described formula (trapezoidal, triangular, … , etc.). As the proposal methodology is based on the trigonometric circle, other trigonometric formulae can be applied. For example, linguistic hedges can be defined using standard trigonometric formulae. Regarding optimization, the initial fuzzy set selection was assumed to be of regular shape (sinusoidal, trapezoidal or triangular). An irregular shape may be required by some systems. Hence, a genetic algorithm was proposed as a methodology to optimize the performance of fuzzy systems by mutating different regular shapes. Findings – A simplified business decision‐making application was described and the proposed selection methodology was explained in the form of an example. Currently, there is no standard for the selection of fuzzy sets as this is dependent on knowledge engineering and the type of application chosen. The proposed methodology offers an easy‐to‐use possible standard which all developers/researchers may adopt irrespective of their application field. Moreover, the proposed methodology may integrate well with object‐oriented technology. Originality/value – The paper presents standardization of the fuzzy sets selection and optimization technique used in any type of management information systems. This will aid all developers and researchers to enhance their technical communication. It would also enhance the simplicity and effectiveness of optimizing the performance of such systems. en_US
dc.language.iso en en_US
dc.title Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SOB en_US
dc.author.idnumber 200301034 en_US
dc.author.department Department of Information Technology and Operations Management (ITOM) en_US
dc.description.embargo N/A en_US
dc.relation.journal Kybernetes en_US
dc.journal.volume 37 en_US
dc.journal.issue 1 en_US
dc.article.pages 166-181 en_US
dc.keywords Fuzzy control en_US
dc.keywords Business analysis en_US
dc.keywords Computers en_US
dc.keywords Decision support systems en_US
dc.keywords Genetics en_US
dc.keywords Cybernetics en_US
dc.identifier.doi http://dx.doi.org/10.1108/03684920810851069 en_US
dc.identifier.ctation Kouatli, I. (2008). Definition and selection of fuzzy sets in genetic-fuzzy systems using the concept of fuzzimetric arcs. Kybernetes, 37(1), 166-181. en_US
dc.author.email issam.kouatli@lau.edu.lb
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://www.emeraldinsight.com/doi/full/10.1108/03684920810851069 en_US

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