Application of Least Square Support Vector Machine based on Particle Swarm Optimization to Chaotic Time Series Prediction

摘要：

The prediction of chaotic time series is performed by least square support vector machine (LS-SVM) based on particle swarm optimization (PSO). The main objective of this approach is to increase the accuracy of the chaotic time series prediction. For the generation performance of LS-SVM depending on a good setting of its parameters, PSO is adopted to choose the global optimum parameters of LS-SVM automatically. The proposed model is applied to the three important chaotic time series including Mackey-Glass time series, Lorenz time series and Henon time series. The simulation results prove the feasibility and effectiveness of the method.

关键词： chaotic time series prediction parameter LS-SVM PSO

作者: Ping Liu Jian Yao

作者单位: School of Automation Science and Electrical Engineering,Beijing University of Aeronautics and Astron 96630 Unit of Peoples Liberation Army Beijing,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2987-2991

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）