Fuzzy rule extraction using robust particle swarm optimization

Automatic fuzzy rule extraction assumes the realization of fuzzy if then rules using a pre-assigned structure rather than an optimal one. In this paper, Particle Swarm Optimization (PSO) is used to simultaneously evolve the structure and the parameters of the fuzzy rule base. However, the existing PSO based adaptation employs randomness, which makes the rate of convergence dependent on the initial states and the end result can not be reproduced repeatedly with a pre-assigned value of iterations. The algorithm has been modified by removing the randomness in parameter learning, making it very robust. The scheme provides the flexibility in extracting the optimal set of fuzzy rules for a prescribed residual error in function approximation and prediction. Simulation studies and the comprehensive analysis demonstrate that an efficient learning technique as well as the structure development of the fuzzy system, can be achieved by the proposed approach.