RBF neural network control system optimized by Particle Swarm Optimization

A RBF neural network control system optimized by Particle Swarm Optimization is proposed. The control system was constructed by two RBF neural network, one was used as identifier and the other was used as controller. The system parameters were optimized by PSO, RBF neural network identified the nonlinear controlled object, the obtained Jacobian information used into RBF controller. Simulation results shows that the system optimized by PSO can get the ideal results of the control to the nonlinear objects, the system has good adaptive capacity and robustness.