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Nonlinear System Control Using a Recurrent Neural Fuzzy Network Based on Reinforcement Particle Swarm Optimization

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3 Author(s)
Cheng-Jian Lin ; Dept. of CSIE, Nat. Chin-Yi Univ. of Technol., Taiping, Taiwan ; Ying-Ming Lin ; Chi-Yung Lee

This paper proposes a recurrent neural fuzzy network with the reinforcement improved particle swarm optimization (R-IPSO) for solving various control problems. The R-IPSO, which consists of structure learning and parameter learning, is also proposed. The structure learning is adopts several sub-swarms to constitute variable fuzzy systems and uses an elite-based structure strategy (ESS) to find suitable the number of fuzzy rules for solving a problem. The parameter learning is adopts an improved particle swarm optimization (IPSO). The examples have been given to illustrate the performance and effectiveness.

Published in:

Computational Intelligence and Design (ISCID), 2010 International Symposium on  (Volume:2 )

Date of Conference:

29-31 Oct. 2010

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