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Nonlinear Noise Reduction of Chaotic Time Series Based on Multi-dimensional Recurrent Least Squares Support Vector Machines

  • Jiancheng Sun
  • Yatong Zhou
  • Yaohui Bai
  • Jianguo Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

Abstract

In order to resolve the noise reduction in chaotic time series, a novel method based on Multi-dimensional version of Recurrent Least Square Support Vector Machine(MDRLS-SVM) is proposed in this paper. By analyzing the relationship between the function approximation and the noise reduction, we realized that the noise reduction can be implemented by the function approximation techniques. On the basis of the MDRLS-SVM and the reconstructed embedding phase theory, the function approximation in the high dimensional embedding phase space is carried out and the noise reduction achieved simultaneously.

Keywords

Support Vector Machine Function Approximation Noise Reduction Phase Plot Chaotic Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiancheng Sun
    • 1
  • Yatong Zhou
    • 1
  • Yaohui Bai
    • 1
  • Jianguo Luo
    • 1
  1. 1.Dept. of communication Eng.Jiangxi University of Finance and Economics. NanchangJiangxiChina

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