Prediction for Chaotic Time Series of Optimized BP Neural Network Based on Modified PSO
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)was proposed.In this modified PSO algorithm,an adaptive mutation operator was proposed in PSO to change positions of the particles plunged in the local optimization.The modified PSO was used to optimize the weights and thresholds of BP neural network,and then BP neural network was trained to search for the optimal solution.The availability of the proposed prediction method was proved by predicting several typical nonlinear systems.The simulation results have shown that the better fitting and higher accuracy are expressed in this improved method.
Prediction Chaos theory BP neural network particle swarm optimization algorithm (PSO)
Li Song Hao Qing Yue Ying-ying Liu Hao-ning
School of Management,Hebei University,Baoding,071002,China School of Electronic and Information Engineering,Hebei University,Baoding,071002,China
国际会议
长沙
英文
697-702
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）