A Fuzzy Neural Network System Based on the Class Cover and the Particle Swarm Optimization

A fuzzy neural network identification model is developed based on the class cover catch digraph and the particle swarm optimazition method. As structure learning of the fuzzy neural network,an improved greedy algorithm is presented for getting the hypersphere class covers with relatively even radii,which is used to partition fuzzy input space and extract fuzzy IF-THEN rules. A weighted Mamdani inference mechanism is improved for the system output and a particle swarm optimization（PSO）-based algorithm is used for optimizing system parameters that improve the system’s correct classification percentages and robustness. Experimental results show that the system is feasible and effective to identify 11 kinds of mineral waters by its taste signals.