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政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 > Item 140.119/64245

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/64245

Title:	Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules
Authors:	Kuo,R.J.;Hong, S.M.;Lin,Y.;Huang, Y.C. 郭人介;洪叔民;黃永成
Contributors:	企管系
Keywords:	Fuzzy neural networks;Continuous genetic algorithms
Date:	2008-08
Issue Date:	2014-02-26 15:38:49 (UTC+8)
Abstract:	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.
Relation:	Neurocomputing, 71(13-15), 2893-2907
Source URI:	http://dx.doi.org/10.1016/j.neucom.2007.07.013
Data Type:	article
DOI 連結:	http://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013
DOI:	10.1016/j.neucom.2007.07.013
Appears in Collections:	[企業管理學系] 期刊論文

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