【摘要】 考虑到BP神经网络模型和PSO粒子群优化算法存在有的收敛速度慢及陷入局部最优的问题，给出了基于融合混沌（Chaos）模型和模拟退火（SA）算法而设计的PSO-BP神经网络模型。将混沌模型和SA算法的优点进行融合并对PSO算法加以改进，防止PSO算法因“早熟”而处于局部最优，从而得出BP神经网络模型的权值与阈值集合。实例验证结果表明，CSAPSO-BP神经网络模型的收敛性高于PSO-BP神经网络模型与SAPSO-BP神经网络模型，其平均绝对百分比误差分别比后两者低25.23%和14.19%。更多还原

【Abstract】 Considering the problems of slow convergence speed and falling into local optimization of the BP neural network model and Particle Swarm Optimization(PSO) algorithm, a PSO-BP neural network model designed based on the fusion of Chaos(C) model and Simulated Annealing(SA) algorithm is proposed. The advantages of the Chaos model and the SA algorithm are fused and the PSO algorithm is improved to prevent the PSO algorithm from being local optimization due to “precociousness”, so as to obtain the weight and threshold set of the BP neural network model. The results of actual case verification show that the convergence of the CSAPSO-BP neural network model is higher than that of the PSO-BP neural network model and the SAPSO-BP neural network model, and the average absolute error ratio is 25.23% and 14.19% lower than the latter two respectively.更多还原