关键词: 后件多项式RBF神经网络, 粒子群优化, 有效性

Abstract: RBF（Radial Basis Function） neural network can be well applied in various fields, the key lies in the selection of network model parameter weight, network center value, base width vector and implicit layer node number. The traditional RBF neural network has the disadvantages of low accuracy, easy to fall into local optimal, slow convergence speed and so on. For these problems, the RBF neural network method is optimized by using particle swarm algorithm, that is, the weight value, network center value, and base width vector value of the RBF neural network containing the latter polynomial are optimized, and the optimal number of implicit nodes is selected. Then the PSOIRBF neural network is proposed. The effectiveness of the proposed algorithm is demonstrated by the simulation of nonlinear controlled objects such as nonlinear models and examples and the analysis of the models.

Key words: post-polynomial RBF neural network, particle swarm optimization, effectiveness