Prediction of chaotic systems with multidimensional recurrent least squares support vector machines
Sun Jian-Cheng (孙建成)ab, Zhou Ya-Tong (周亚同)a, Luo Jian-Guo (罗建国)a
a Department of Communication Engineering, University of Finance and Economics, Nanchang 330013, China; b Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Abstract 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.
Received: 25 December 2005
Revised: 07 March 2006
Accepted manuscript online:
Fund: Project supported by the National Natural Science Foundation of China (Grant
No 90207012).
Cite this article:
Sun Jian-Cheng (孙建成), Zhou Ya-Tong (周亚同), Luo Jian-Guo (罗建国) Prediction of chaotic systems with multidimensional recurrent least squares support vector machines 2006 Chinese Physics 15 1208
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