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題名 Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules
作者 Kuo,R.J. ; Hong, S.M. ; Lin,Y. ; Huang, Y.C.
郭人介;洪叔民;黃永成
貢獻者 企管系
關鍵詞 Fuzzy neural networks; Continuous genetic algorithms
日期 2008-08
上傳時間 26-二月-2014 15:38:49 (UTC+8)
摘要 This 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.
關聯 Neurocomputing, 71(13-15), 2893-2907
資料來源 http://dx.doi.org/10.1016/j.neucom.2007.07.013
資料類型 article
DOI http://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013
dc.contributor 企管系en_US
dc.creator (作者) Kuo,R.J. ; Hong, S.M. ; Lin,Y. ; Huang, Y.C.en_US
dc.creator (作者) 郭人介;洪叔民;黃永成zh_TW
dc.date (日期) 2008-08en_US
dc.date.accessioned 26-二月-2014 15:38:49 (UTC+8)-
dc.date.available 26-二月-2014 15:38:49 (UTC+8)-
dc.date.issued (上傳時間) 26-二月-2014 15:38:49 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64245-
dc.description.abstract (摘要) This 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.extent 397431 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Neurocomputing, 71(13-15), 2893-2907en_US
dc.source.uri (資料來源) http://dx.doi.org/10.1016/j.neucom.2007.07.013en_US
dc.subject (關鍵詞) Fuzzy neural networks; Continuous genetic algorithmsen_US
dc.title (題名) Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rulesen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.neucom.2007.07.013en_US
dc.doi.uri (DOI) http://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013en_US