Quantification of R-fuzzy sets
| dc.contributor.author | Khuman, A. S. | en |
| dc.contributor.author | Yang, Yingjie | en |
| dc.contributor.author | John, Robert, 1955- | en |
| dc.date.acceptance | 2016-02-06 | |
| dc.date.accessioned | 2016-03-23T11:38:07Z | |
| dc.date.available | 2016-03-23T11:38:07Z | |
| dc.date.issued | 2016-02-20 | |
| dc.description.abstract | The 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.identifier.citation | Khuman, A.S., Yang, Y. and John, R. (2016) Quantification of R-fuzzy sets. Expert Systems with Applications, 55, pp. 374-387 | en |
| dc.identifier.doi | https://doi.org/10.1016/j.eswa.2016.02.010 | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.uri | http://hdl.handle.net/2086/11682 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | Pergamon | en |
| dc.researchgroup | Centre for Computational Intelligence | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | R-fuzzy sets | en |
| dc.subject | Rough sets | en |
| dc.subject | Fuzzy membership | en |
| dc.subject | Significance | en |
| dc.subject | Type-2 equivalence | en |
| dc.title | Quantification of R-fuzzy sets | en |
| dc.type | Article | en |
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