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dc.contributor.authorKhuman, A. S.en
dc.contributor.authorYang, Yingjieen
dc.contributor.authorJohn, Robert, 1955-en
dc.date.accessioned2016-03-23T11:38:07Z
dc.date.available2016-03-23T11:38:07Z
dc.date.issued2016-08-15
dc.identifier.citationKhuman, A.S., Yang, Y. and John, R. (2016) Quantification of R-fuzzy sets. Expert Systems with Applications, 55, pp. 374-387en
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/2086/11682
dc.description.abstractThe main aim of this paper is to connect R-fuzzy sets and type-2 fuzzy sets, so as to provide a practical means to express complex uncertainty without the associated difficulty of a type-2 fuzzy set. The paper puts forward a significance measure, to provide a means for understanding the importance of the membership values contained within an R-fuzzy set. The pairing of an R-fuzzy set and the significance measure allows for an intermediary approach to that of a type-2 fuzzy set. By inspecting the returned significance degree of a particular membership value, one is able to ascertain its true significance in relation, relative to other encapsulated membership values. An R-fuzzy set coupled with the proposed significance measure allows for a type-2 fuzzy equivalence, an intermediary, all the while retaining the underlying sentiment of individual and general perspectives, and with the adage of a significantly reduced computational burden. Several human based perception examples are presented, wherein the significance degree is implemented, from which a higher level of detail can be garnered. The results demonstrate that the proposed research method combines the high capacity in uncertainty representation of type-2 fuzzy sets, together with the simplicity and objectiveness of type-1 fuzzy sets. This in turn provides a practical means for problem domains where a type-2 fuzzy set is preferred but difficult to construct due to the subjective type-2 fuzzy membership.en
dc.language.isoenen
dc.publisherPergamonen
dc.subjectR-fuzzy setsen
dc.subjectRough setsen
dc.subjectFuzzy membershipen
dc.subjectSignificanceen
dc.subjectType-2 equivalenceen
dc.titleQuantification of R-fuzzy setsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.eswa.2016.02.010
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
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