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Conference Paper: A new fuzzy classifier with triangular membership functions

TitleA new fuzzy classifier with triangular membership functions
Authors
KeywordsComputers
Artificial intelligence
Issue Date1997
PublisherIEEE.
Citation
International Conference on Neural Networks Proceedings, Houston, TX, 9-12 June 1997, v. 1, p. 479-484 How to Cite?
AbstractFuzzy logic is widely applied in control and modeling for its robustness, simplicity and clarity. It is also applied in classifier design with rules directly generated from numerical data. Some available rule generation methods, however, are either too complicated to implement or impractical for high dimensions. In this paper, we propose a new fuzzy classifier architecture. At the very beginning the training data is clustered at the input space. Fuzzy sets are then defined based on these clusters with triangular membership function. The outputs in the rule conclusion are initially determined by the “normalized vote” in the corresponding cluster. Fuzzy sets and conclusions can be adjusted through training. The proposed fuzzy system is simple in structure, and can be fast trained and easily implemented. Its classification performance is generally better than artificial neural network.
Persistent Identifierhttp://hdl.handle.net/10722/46013
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYang, YSen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorHung, Nen_HK
dc.date.accessioned2007-10-30T06:40:37Z-
dc.date.available2007-10-30T06:40:37Z-
dc.date.issued1997en_HK
dc.identifier.citationInternational Conference on Neural Networks Proceedings, Houston, TX, 9-12 June 1997, v. 1, p. 479-484en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46013-
dc.description.abstractFuzzy logic is widely applied in control and modeling for its robustness, simplicity and clarity. It is also applied in classifier design with rules directly generated from numerical data. Some available rule generation methods, however, are either too complicated to implement or impractical for high dimensions. In this paper, we propose a new fuzzy classifier architecture. At the very beginning the training data is clustered at the input space. Fuzzy sets are then defined based on these clusters with triangular membership function. The outputs in the rule conclusion are initially determined by the “normalized vote” in the corresponding cluster. Fuzzy sets and conclusions can be adjusted through training. The proposed fuzzy system is simple in structure, and can be fast trained and easily implemented. Its classification performance is generally better than artificial neural network.en_HK
dc.format.extent378311 bytes-
dc.format.extent13817 bytes-
dc.format.extent8841 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputersen_HK
dc.subjectArtificial intelligenceen_HK
dc.titleA new fuzzy classifier with triangular membership functionsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=1&spage=479&epage=484&date=1997&atitle=A+new+fuzzy+classifier+with+triangular+membership+functionsen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNN.1997.611715en_HK
dc.identifier.hkuros27271-

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