| 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) | 2008-08 | en_US |
| dc.date.accessioned | 26-Feb-2014 15:38:49 (UTC+8) | - |
| dc.date.available | 26-Feb-2014 15:38:49 (UTC+8) | - |
| dc.date.issued (上傳時間) | 26-Feb-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), 2893-2907 | en_US |
| dc.source.uri (資料來源) | http://dx.doi.org/10.1016/j.neucom.2007.07.013 | en_US |
| dc.subject (關鍵詞) | Fuzzy neural networks; Continuous genetic algorithms | en_US |
| dc.title (題名) | Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1016/j.neucom.2007.07.013 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013 | en_US |
