Design of Fuzzy Rule-Based Systems through Particle Swarm Optimization
Hsuan-Ming Feng · 2005
In this paper, an innovative swarm intelligent method called particle swarm optimization (PSO) is developed to generate fuzzy systems for approximating a nonlinear function and solving the car-pole system problem. The PSO algorithm is an efficient stochastic evolutionary learning technique to deal with complex and global optimization problems. The advantage of the PSO includes easy implementation, fast convergence ability and lower computational load. This paper illustrates the perfect PSO algorithm in detail with the simulation to automatically tune some appropriate parameters of fuzzy rule-based systems. Computer simulation results on two nonlinear problems are derived to demonstrate the efficiency of PSO.