计算机工程与应用Issue(2)：224-229,264,7.DOI:10.3778/j.issn.1002-8331.1306-0342
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 PSO
摘要
Abstract
BP neural network for forecasting has low speed of convergence, low precision and easily falling into the local minimum state. An improved prediction method of optimized BP neural network based on Improved Particle Swarm Opti-mization algorithm(IPSO)is proposed. The IPSO algorithm adopts modified adaptive inertia weight and adaptive acceler-ation coefficients to optimize the weights and thresholds of BP neural network. Then BP neural network is trained to search for the optimal solution. This experiment is done with several typical nonlinear systems. The results demonstrate that the improved method has faster convergence speed, higher accuracy and not easily falling into the local minimum state.关键词
混沌时间序列/混沌预测/反向传播(BP)神经网络/粒子群算法Key words
chaotic time series/prediction of chaos/Back Propagation(BP)neural network/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
卢辉斌,李丹丹,孙海艳..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,2015,(2):224-229,264,7.基金项目
河北省教育厅2007年科研计划项目（No.2007493）。 （）
