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

Author

Lin, Cheng-Jian ; Lin, Ying-Ming ; Lee, Chi-Yung

Author_Institution

Dept. of CSIE, Nat. Chin-Yi Univ. of Technol., Taiping, Taiwan

Volume

2

fYear

2010

fDate

29-31 Oct. 2010

Firstpage

196

Lastpage

200

Abstract

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.

Keywords

fuzzy neural nets; fuzzy systems; learning (artificial intelligence); neurocontrollers; nonlinear control systems; particle swarm optimisation; recurrent neural nets; ESS; R-IPSO; elite-based structure strategy; fuzzy system; nonlinear control system; parameter learning; recurrent neural fuzzy network; reinforcement improved particle swarm optimization; structure learning; Neural fuzzy network; control; elite-based structure strategy; particle swarm optimization; recurrent network; reinforcement learning;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-8094-4

Type

conf

DOI

10.1109/ISCID.2010.139

Filename

5692768