Nonlinear Chaotic States Prediction Based on LS-SVM

摘要：

Chaos phenomena can be found in many fields. Chaos prediction has played an important role in the study of chaos system. However, it is difficult to predict chaos. Previous studies have no perfect accuracy in forecasting, furthermore previous approaches have no well learning. A method is proposed to predict the states of chaos based on the algorithm of LS-SVM (least square support vectors machine) in this study. Our approach is based on reconstruct phase space coming from the Takens embedding theorem. In this approach, the data are divided into two parts; the first part is used to train the model, another part is used as the test set. The learning model can be obtained by moving the window, whose width is n, along the axis time. The n relates to the capacity of the input points, which has the best district. Theory analysis has also been done. The results show that the method based on LS-SVM, which has better performance, can be used effectively in chaos prediction by numerical experiments.

关键词： nonlinear chaos prediction LS-SVM phase space

作者: Qiang Jiang Menglin Wang Gao Zheng Wangang Wang

作者单位: School of electrical engineering, Southwest Jiaotong University Chengdu, 610031, P.R.China

会议类型: 国际会议

会议名称: The 10th International Conference on Intelligent Technologies(第十届智慧科技国际会议 InTech09)

会议地点: 桂林

会议语种:英文

页码: 432-435

在线出版日期: 2009-12-12（万方平台首次上网日期，不代表论文的发表时间）