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dc.contributor.authorChao, CTen_US
dc.contributor.authorChen, YJen_US
dc.contributor.authorTeng, CCen_US
dc.date.accessioned2014-12-08T15:02:44Z-
dc.date.available2014-12-08T15:02:44Z-
dc.date.issued1996-04-01en_US
dc.identifier.issn1083-4419en_US
dc.identifier.urihttp://dx.doi.org/10.1109/3477.485887en_US
dc.identifier.urihttp://hdl.handle.net/11536/1378-
dc.description.abstractThis paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate redundant fuzzy logical rules, so that the number of rules in the resulting fuzzy inference system will be reduced. Moreover, a fuzzy similarity measure for fuzzy sets that indicates the degree to which two fuzzy sets are equal is applied to combine similar input linguistic term nodes. Thus we obtain a method for reducing the complexity of a fuzzy neural network. We also design a new and efficient on-line initialization method for choosing the initial parameters of the FNNS. a computer simulation is presented to illustrate the performance and applicability of the proposed FNNS. The results indicates that the FNNS still has desirable performance under fewer fuzzy logical rules and adjustable parameters.en_US
dc.language.isoen_USen_US
dc.titleSimplification of fuzzy-neural systems using similarity analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/3477.485887en_US
dc.identifier.journalIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICSen_US
dc.citation.volume26en_US
dc.citation.issue2en_US
dc.citation.spage344en_US
dc.citation.epage354en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:A1996UD02400015-
dc.citation.woscount84-
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