Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44314
標題: Automatic construction of feedforward/recurrent fuzzy systems by clustering-aided simplex particle swarm optimization
作者: Juang, C.F.
莊家峰
Chung, I.F.
Hsu, C.H.
關鍵字: fuzzy neural network
recurrent fuzzy system
swarm intelligence
simplex method
plant modeling
time series prediction
temperature
control
genetic algorithms
design
network
期刊/報告no:: Fuzzy Sets and Systems, Volume 158, Issue 18, Page(s) 1979-1996.
摘要: This paper proposes a new approach for automating the structure and parameter learning of fuzzy systems by clustering-aided simplex particle swarm optimization, called CSPSO. Unlike most evolutionary fuzzy systems, where the structure of the fuzzy system is assigned in advance, an on-line fuzzy clustering approach is proposed for system structure learning. This structure learning not only helps determine the number of rules automatically, but also avoids the generation of highly similar fuzzy sets on each input variable. In addition, it improves subsequent parameter learning performance by assigning suitable initial locations of the fuzzy sets on each input variable. Once a new rule is generated, the corresponding parameters are further tuned by the hybrid of the simplex method and particle swarm optimization (PSO). In CSPSO, each fuzzy system corresponds to a particle in PSO, and the idea of the simplex method is incorporated to improve PSO searching performance. To verify the performance of CSPSO, two simulations on feedforward fuzzy systems design are performed. In addition, design of a recurrent fuzzy controller for a practical experiment on water bath temperature control is performed. Comparisons with other design approaches are also made in these examples. (c) 2007 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11455/44314
ISSN: 0165-0114
文章連結: http://dx.doi.org/10.1016/j.fss.2007.04.009
Appears in Collections:電機工程學系所

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