计算机工程与应用2013，Vol.49Issue(6)：245-248,270,5.DOI:10.3778/j.issn.1002-8331.1108-0081
改进PSO优化BP神经网络的混沌时间序列预测
Prediction for chaotic time series of optimized BP neural network based on modified PSO
摘要
Abstract
In order to improve forecasting model accuracy of BP neural network, an improved prediction method of optimized BP neural network based on modified Particle Swarm Optimization algorithm (PSO) is proposed. In this modified PSO algorithm, an adaptive mutation operator is proposed in PSO to change positions of the particles which plunge in the local optimization. The modified PSO is used to optimize the weights and thresholds of BP neural network, and then BP neural network is trained to search for the optimal solution. The availability of the proposed prediction method is proven by predicting several typical nonlinear systems. The simulation results have shown that the better fitting and higher accuracy arc expressed in this improved method.关键词
预测/混沌理论/反向传播(BP)神经网络/粒子群算法Key words
prediction/ chaos theory/ Back Propagation(BP) neural network/ Particle Swarm Optimization(PSO)分类
信息技术与安全科学引用本文复制引用
李松,刘力军,刘颖鹏..改进PSO优化BP神经网络的混沌时间序列预测[J].计算机工程与应用,2013,49(6):245-248,270,5.基金项目
河北省自然科学基金(No.E2012201002) （No.E2012201002）
河北省高等学校人文社会科学研究重点项目(No.SKZD2011106). （No.SKZD2011106）
