Prediction of chaotic systems with multidimensional recurrent least squares support vector machines |
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| Authors: | Sun Jian-Cheng Zhou Ya-Tong and Luo Jian-Guo |
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| Affiliation: | Department of Communication Engineering, University of Finance and Economics, Nanchang 330013, China; Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049, China |
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| 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. |
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| Keywords: | chaotic systems support vector
machines least squares noise |
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