FCM-Based QPSO for Evolutionary Fuzzy-System Design

This paper proposes a FCM-based QPSO algorithm for evolutionary fuzzy-system design. The objective of this paper is to learn TSK type fuzzy rules with high accuracy. In the designed fuzzy system, data is firstly clustered into classes by fuzzy c-means algorithm so that each rule defines its own fuzzy sets, the number of fuzzy rules is also determined by the number of clusters. Then Quantum-behaved particle swarm optimization learning algorithm then used for optimising the parameters of the fuzzy system. We illustrates the algorithm in details with computer simulation to solve nonlinear problems and compare the results between basic PSO and our algorithm.