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dc.contributor.authorChung, IFen_US
dc.contributor.authorLin, CJen_US
dc.contributor.authorLin, CTen_US
dc.date.accessioned2014-12-08T15:45:16Z-
dc.date.available2014-12-08T15:45:16Z-
dc.date.issued2000-05-16en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://hdl.handle.net/11536/30516-
dc.description.abstractThis paper addresses the structure and an associated learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. A structure/parameter learning algorithm, called FALCON-GA, is proposed for constructing the FALCON automatically. The FALCON-GA is a three-phase hybrid learning algorithm. In the first phase, the fuzzy ART algorithm is used to do fuzzy clustering in the input/output spaces according to the supervised training data. In the second phase, the genetic algorithm (GA) is used to find proper fuzzy logic rules by associating input clusters and output clusters. Finally, in the third phase, the backpropagation algorithm is used for tuning input:output membership functions. Hence, the FALCON GA combines the backpropagation algorithm for parameter learning and both the fuzzy ART and GAs for structure learning. It can partition the input/output spaces, tune membership functions and find proper fuzzy logic rules automatically. The proposed FALCON has two important features. First, it reduces the combinatorial demands placed by the standard methods for adaptive linearization of a system. Second, the FALCON is a highly autonomous system. In its learning scheme, only the training data need to be provided from the outside world. The users need not give the initial fuzzy partitions, membership functions and fuzzy logic rules. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system. (C) 2000 Elsevier Science B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectfuzzy ARTen_US
dc.subjectexpert systemsen_US
dc.subjectmembership functionen_US
dc.subjectspace partitionen_US
dc.subjectbackpropagationen_US
dc.subjectchaotic sequenceen_US
dc.titleA GA-based fuzzy adaptive learning control networken_US
dc.typeArticleen_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume112en_US
dc.citation.issue1en_US
dc.citation.spage65en_US
dc.citation.epage84en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000086088800005-
dc.citation.woscount33-
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