Interval type-2 fuzzy membership function generation methods for pattern recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Byung-In | - |
dc.contributor.author | Rhee, Frank Chung-Hoon | - |
dc.date.accessioned | 2021-06-23T15:37:00Z | - |
dc.date.available | 2021-06-23T15:37:00Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2009-06 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/41101 | - |
dc.description.abstract | Type-2 fuzzy sets (T2 FSs) have been shown to manage uncertainty more effectively than T1 fuzzy sets (T1 FSs) in several areas of engineering [4,6-12,15-18,21-27,30]. However, computing with T2 FSs can require undesirably large amount of computations since it involves numerous embedded T2 FSs. To reduce the complexity, interval type-2 fuzzy sets (IT2 FSs) can be used, since the secondary memberships are all equal to one [21]. In this paper, three novel interval type-2 fuzzy membership function (IT2 FMF) generation methods are proposed. The methods are based on heuristics, histograms, and interval type-2 fuzzy C-means. The performance of the methods is evaluated by applying them to back-propagation neural networks (BPNNs). Experimental results for several data sets are given to show the effectiveness of the proposed membership assignments. (C) 2008 Elsevier Inc. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.title | Interval type-2 fuzzy membership function generation methods for pattern recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Rhee, Frank Chung-Hoon | - |
dc.identifier.doi | 10.1016/j.ins.2008.04.009 | - |
dc.identifier.scopusid | 2-s2.0-64549084554 | - |
dc.identifier.wosid | 000266216100005 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.179, no.13, pp.2102 - 2122 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 179 | - |
dc.citation.number | 13 | - |
dc.citation.startPage | 2102 | - |
dc.citation.endPage | 2122 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | LOGIC SYSTEMS | - |
dc.subject.keywordAuthor | Fuzzy membership function generation | - |
dc.subject.keywordAuthor | Interval type-2 fuzzy sets | - |
dc.subject.keywordAuthor | Fuzzy C-means | - |
dc.subject.keywordAuthor | Footprint of uncertainty | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0020025508001369?via%3Dihub | - |
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