关键词: 神经网络权值, 粒子群优化算法, 动态惯性权重, 变异与交叉, 有效性

Abstract: In view of inaccurately selecting problem of neural networks weights, the method of improved particle swarm optimization algorithm combined with BP neural network to select weights dynamically is proposed. The improved particle swarm optimization algorithm applied dynamic inertia weight, cognitive parameter and social parameter is restrained each other. Simultaneously, the improved particle swarm optimization algorithm combined with differential evolution algorithm to make particles have mutation and crossover operation, maintain the particle diversity. The IPSONN neural network model based on the improved particle swarm optimization algorithm and BP neural network is constructed. The model is applied in forecasting the wine quality. Experiments prove the effectiveness of the IPSONN model from three aspects：training accuracy, correct rate and particle diversity.

Key words: neural network weights, particle swarm optimization algorithm, dynamic inertia weight, mutation and crossover, effectiveness