关键词: BP网络, PSO粒子群算法, 传递函数, 逼近, 泛化

Abstract: This paper employs the PSO algorithm to update the weights，the biases and the transfer function’s coefficients of the hidden layer in the neural network.As to the phenomena of good approximation and bad generalization，the MSE of the training set and the MSW of the weights are integrated into the fitness goal.In the experiment，the GPSO-BP algorithm which optimizes the coefficients of the transfer function and has the small weights and thresholds is better than the BP algorithm and the PSO-BP algorithm in terms of the mean correct recognition and the stability.

Key words: Back Propagation（BP） neural network, Particle Swarm Optimization（PSO） algorithm, transfer function, approximation, generalization