Application of Particle Swarm Optimization in Fussy Neural Networks

Particle swarm optimization algorithm is a global optimization technique and a new technology base on swarm brainpower. This ideology comes from manpower anima and evolvement calculation theory. Its algorithm is simple for implement and excellent for application. Particle follow the one which is the best it found in the whole swarm to complete optimize. To solve the adjustable capability of fuzzy controlment and combine with the characteristic of nerve network, so fuzzy neural networks based on particle swarm optimization is designed in this paper. A nonlinear system is identified by the fussy neural networks. The distinguish process of fuzzy nerve network is confirming the precondition parameter and conclusion parameter. Simulation result indicates the great effect and potential in optimization of fuzzy nerve network. Base on this arithmetic's speediness and availability, it can be use to practical field.