Title :
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
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2010 International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-8094-4
DOI :
10.1109/ISCID.2010.139
