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https://ah.lib.nccu.edu.tw/handle/140.119/64245
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: | Aug-2008 | 上傳時間: | 26-Feb-2014 | 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 |
Appears in Collections: | 期刊論文 |
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