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

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.