Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/64245
DC FieldValueLanguage
dc.contributor企管系en_US
dc.creatorKuo,R.J. ; Hong, S.M. ; Lin,Y. ; Huang, Y.C.en_US
dc.creator郭人介;洪叔民;黃永成zh_TW
dc.date2008-08en_US
dc.date.accessioned2014-02-26T07:38:49Z-
dc.date.available2014-02-26T07:38:49Z-
dc.date.issued2014-02-26T07:38:49Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/64245-
dc.description.abstractThis study proposes a fuzzy neural network (FNN) that can process both fuzzy inputs and outputs. The continuous genetic algorithm (CGA) is employed to enhance its performance. Both the simulation and real-world problem results show that the proposed CGA-based FNN can obtain the relationship between fuzzy inputs and outputs. CGA can not only shorten the training time but also increase the accuracy for the FNN.en_US
dc.format.extent397431 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen_US-
dc.relationNeurocomputing, 71(13-15), 2893-2907en_US
dc.source.urihttp://dx.doi.org/10.1016/j.neucom.2007.07.013en_US
dc.subjectFuzzy neural networks; Continuous genetic algorithmsen_US
dc.titleContinuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rulesen_US
dc.typearticleen
dc.identifier.doi10.1016/j.neucom.2007.07.013en_US
dc.doi.urihttp://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013en_US
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.openairetypearticle-
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