关键词: 径向基神经网络, 资源分配网络, 剪枝策略, 粒子群优化

Abstract: An improved method for RBF neural network parameters optimization is proposed. The number of nodes in the hidden layer is determined by using RAN（Resource Allocating Network）, meanwhile strategy of pruning is introduced to remove those hidden units which make insignificant contribution to overall network output. Central position, width and weight of the neural network are optimized by the improved PSO（Particle Swarm Optimization） algorithm, so as to obtain the appropriate structure and control parameters. The new algorithm is used to predict the model of CSTR, and the result indicates that RBF neural network optimized by this algorithm has a smaller structure and high generalization ability.

Key words: radial basis function neural network, resource allocating network, strategy of pruning, particle swarm optimization