Prediction of the chaotic time series based on chaotic simulated annealing and support vector machine

摘要：

The regression accuracy and generalization performance of the support vector regression (SVR) model depend on a proper setting of its parameters. An optimal selection approach of SVR parameters was put forward based on chaotic simulated annealing algorithm (CSAA), the key parameters C and e of SVM and the radial basis kernel parameter g were optimized within the global scope. The support vector regression model was established for chaotic time series prediction by using the optimum parameters. The time series of Lorenz system was used to testify the effectiveness of the model. The root mean square error of prediction reached 8.756 x 10*. Simulation results show that the optimal selection approach based on CSAA is available and the CSAA-SVR model can predict the chaotic time series accurately.

关键词： support vector machine chaotic simulated annealing algorithm chaotic time series prediction phase space reconstruction

作者: Hu Yuxia Zhang Hongtao

作者单位: College of Electric Engineering Zhengzhou University Zhengzhou, China Institute of Electric power North China Institute of Water Conservancy and Hydroelectric Power Zheng

会议类型: 国际会议

会议名称: 2010 Second Asia-Pacific Conference on Information Processing(2010年第二届亚太地区信息处理国际会议 APCIP 2010)

会议地点: 南昌

会议语种:英文

页码: 170-173

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）