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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
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