Prediction of chaotic systems with multidimensional recurrent least squares support vector machines  

作　　者：孙建成 周亚同 罗建国 

机构地区：[1]Department of Communication Engineering, University of Finance and Economics, Nanchang 330013, China [2]Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049, China

出　　处：《Chinese Physics B》2006年第6期1208-1215,共8页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China （Grant No 90207012）.

摘　　要：In this paper, we propose a multidimensional version of recurrent least squares support vector machines （MDRLS- SVM） to solve the problem about the prediction of chaotic system. To acquire better prediction performance, the high-dimensional space, which provides more information on the system than the scalar time series, is first reconstructed utilizing Takens＇s embedding theorem. Then the MDRLS-SVM instead of traditional RLS-SVM is used in the high- dimensional space, and the prediction performance can be improved from the point of view of reconstructed embedding phase space. In addition, the MDRLS-SVM algorithm is analysed in the context of noise, and we also find that the MDRLS-SVM has lower sensitivity to noise than the RLS-SVM.In this paper, we propose a multidimensional version of recurrent least squares support vector machines （MDRLS- SVM） to solve the problem about the prediction of chaotic system. To acquire better prediction performance, the high-dimensional space, which provides more information on the system than the scalar time series, is first reconstructed utilizing Takens＇s embedding theorem. Then the MDRLS-SVM instead of traditional RLS-SVM is used in the high- dimensional space, and the prediction performance can be improved from the point of view of reconstructed embedding phase space. In addition, the MDRLS-SVM algorithm is analysed in the context of noise, and we also find that the MDRLS-SVM has lower sensitivity to noise than the RLS-SVM.

关 键 词：chaotic systems support vector machines least squares noise 

分 类 号：O41[理学—理论物理]