Chaos optimization method of S VM parameters selection for chaotic time series forecasting

摘要：

For support vector regression (SVR), the setting of key parameters is very important, which determines the regression accuracy and generalization performance of SVR model. In this paper, an optimal selection approach for SVR parameters was put forward based on imitative scale optimization algorithm (MSCOA), the key parameters C and e of SVM and the radial basis kernel parameter g were optimized within the global scopes. 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 RMSE = 3.0335 ×10-3. Simulation results show that the optimal selection approach based on MSCOA is an effective approach and the MSCOA-SVR model has a good performance for chaotic time series forecasting.

关键词： support vector machine Mutative scale chaos optimization algorithm chaotic time series prediction phase space reconstruction,parameter selection

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: 459-462

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）