Abstract: A method of radial basis function(RBF) neural network algorithm based on particle swarm optimization(PSO) algorithm. The first to determine units’ number in RBF layer using subtractive clustering method, the second to optimize central position and directional width based on PSO algorithm, and the last to train RBF neural network combine with least-square method. The new training algorithm is used to predict chaotic time series, and the simulation results are efficiency.

Key words: particle swarm optimization, subtractive clustering method, chaotic time series, least-square method, RBF neural network

摘要： 提出了一种基于粒子群优化（PSO）算法的径向基函数（RBF）神经网络学习方法，首先利用减聚类算法确定网络径向基层的单元数，再用PSO对基中心和宽度进行优化，并与最小二乘法相结合训练RBF神经网络。将此算法用于混沌时间序列的预测，实例仿真表明此方法是有效的。

关键词: 粒子群, 减聚类算法, 混沌时间序列, 最小二乘法, 径向基函数神经网络