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Conference Paper: A new fuzzy approach for pattern recognition with application to EMG classification

TitleA new fuzzy approach for pattern recognition with application to EMG classification
Authors
KeywordsComputers
Artificial intelligence
Issue Date1996
PublisherIEEE.
Citation
International Conference on Neural Networks Proceedings, Washington, DC, USA, 3-6 June 1996, v. 2, p. 1109-1114 How to Cite?
AbstractA fuzzy logic system with center average defuzzifier, product-inference rule, nonsingleton fuzzifier and Gauss membership function is discussed. The fuzzy sets are initially defined by the cluster parameters from the Basic ISO-DATA algorithm on input space. The system is then trained via back error propagation algorithm so that the fuzzy sets are fine-tuned. The system is applied to functional EMG classification and compared with its ANN counterpart. It is superior to the latter in at least three points: higher recognition rate; insensitive to over-training; and more consistent outputs thus having higher reliability.
Persistent Identifierhttp://hdl.handle.net/10722/46008
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYang, YSen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorZhang, YTen_HK
dc.contributor.authorParker, PAen_HK
dc.date.accessioned2007-10-30T06:40:32Z-
dc.date.available2007-10-30T06:40:32Z-
dc.date.issued1996en_HK
dc.identifier.citationInternational Conference on Neural Networks Proceedings, Washington, DC, USA, 3-6 June 1996, v. 2, p. 1109-1114en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46008-
dc.description.abstractA fuzzy logic system with center average defuzzifier, product-inference rule, nonsingleton fuzzifier and Gauss membership function is discussed. The fuzzy sets are initially defined by the cluster parameters from the Basic ISO-DATA algorithm on input space. The system is then trained via back error propagation algorithm so that the fuzzy sets are fine-tuned. The system is applied to functional EMG classification and compared with its ANN counterpart. It is superior to the latter in at least three points: higher recognition rate; insensitive to over-training; and more consistent outputs thus having higher reliability.en_HK
dc.format.extent634113 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1996 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.subjectComputersen_HK
dc.subjectArtificial intelligenceen_HK
dc.titleA new fuzzy approach for pattern recognition with application to EMG classificationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=2&spage=1109&epage=1114&date=1996&atitle=A+new+fuzzy+approach+for+pattern+recognition+with+application+to+EMG+classificationen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNN.1996.549053en_HK
dc.identifier.hkuros27123-

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