Particle swarm optimization learning fuzzy systems design

A particle swarm optimization (PSO) learning algorithm is proposed in our research to generate fuzzy systems for balancing the car-pole system and approximating a nonlinear function. Trust to the devoted feature of PSO, i.e. simple implementation, fast convergence and small computational load, this paper illustrates the perfect PSO algorithm in detail with computer simulation to automatically tune some adjustable parameters of fuzzy systems. Computer simulation results on two nonlinear problems are derived to demonstrate the powerful PSO learning algorithm.