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dc.contributor.authorJuang, C.F.en_US
dc.description.abstractA genetic recurrent fuzzy system which automates the design of recurrent fuzzy networks by a coevolutionary genetic algorithm with divide-and-conquer technique (CGA-DC) is proposed in this paper. To solve temporal problems, the recurrent fuzzy network constructed from a series of recurrent fuzzy if-then rules is adopted. In the CGA-DC, based on the structure of a recurrent fuzzy network, the design problem is divided into the design of individual subrules, including spatial and temporal, and that of the whole network. Then, three populations are created, among which two are created for spatial and temporal subrules searches, and the other for the whole network search. Evolution of the three populations are performed independently and concurrently to achieve a good design performance. To demonstrate the performance of CGA-DC, temporal problems on dynamic plant control and chaotic system processing are simulated. In this way, the efficacy and efficiency of CGA-DC can be evaluated as compared with other genetic-algorithm-based design approaches.en_US
dc.relationIeee Transactions on Systems Man and Cybernetics Part C-Applications and Reviewsen_US
dc.relation.ispartofseriesIeee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Volume 35, Issue 2, Page(s) 249-254.en_US
dc.subjectdynamic plant controlen_US
dc.subjectelite strategyen_US
dc.subjectfuzzy controlen_US
dc.subjectrecurrent neural networken_US
dc.subjectsymbiotic evolutionen_US
dc.titleGenetic recurrent fuzzy system by coevolutionary computation with divide-and-conquer techniqueen_US
dc.typeJournal Articlezh_TW
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