Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/64245
DC Field | Value | Language |
---|---|---|
dc.contributor | 企管系 | en_US |
dc.creator | Kuo,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.date | 2008-08 | en_US |
dc.date.accessioned | 2014-02-26T07:38:49Z | - |
dc.date.available | 2014-02-26T07:38:49Z | - |
dc.date.issued | 2014-02-26T07:38:49Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/64245 | - |
dc.description.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. | en_US |
dc.format.extent | 397431 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en_US | - |
dc.relation | Neurocomputing, 71(13-15), 2893-2907 | en_US |
dc.source.uri | http://dx.doi.org/10.1016/j.neucom.2007.07.013 | en_US |
dc.subject | Fuzzy neural networks; Continuous genetic algorithms | en_US |
dc.title | Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules | en_US |
dc.type | article | en |
dc.identifier.doi | 10.1016/j.neucom.2007.07.013 | en_US |
dc.doi.uri | http://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013 | en_US |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en_US | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | restricted | - |
item.openairetype | article | - |
Appears in Collections: | 期刊論文 |
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28932907.pdf | 388.12 kB | Adobe PDF2 | View/Open |
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