Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/43998
標題: Genetic recurrent fuzzy system by coevolutionary computation with divide-and-conquer technique
作者: Juang, C.F.
莊家峰
關鍵字: dynamic plant control
elite strategy
fuzzy control
premature
convergence
recurrent neural network
symbiotic evolution
network
期刊/報告no:: Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Volume 35, Issue 2, Page(s) 249-254.
摘要: A 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.
URI: http://hdl.handle.net/11455/43998
ISSN: 1094-6977
文章連結: http://dx.doi.org/10.1109/tsmcc.2004.841901
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