关键词: 粒子群算法, 径向基神经网络, 惯性权重因子

Abstract: To improve the method to interpose structure and parameters of neural network, a novel Radial Basis Function（RBF） neural network method based on Improved Particle Swarm Optimization（IMPSO） is proposed. The convergence speed of this algorithm and the capacity of searching global optimum value are increased through adjusting inertia weight factor dynamically. The experiments show that the neural network based on IMPSO algorithm is superior to self-organizing center selected algorithm and standard PSO algorithm in the capacity of function approximation, and enhances the generalization and the optimized effect of the network. The capacity of solving nonlinear problems of this algorithm is enhanced effectively.

Key words: Particle Swarm Optimization（PSO）, Radial Basis Function Neural Network（RBFNN）, inertia weight factor