Modelling of Chaotic Systems with Recurrent Least Squares Support Vector Machines Combined with Stationary Wavelet Transform

  • Jiancheng Sun
  • Lun Yu
  • Guang Yang
  • Congde Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3497)


A new strategy for modeling of chaotic systems is presented, which is based on the combination of the stationary wavelet transform and Recurrent Least Squares Support Vector Machines (RLS-SVM). The stationary wavelet transform provide a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. The similarity of dynamic invariants between the origin and generated time series shows that the proposed method can capture the dynamics of the chaotic time series effectively.


Support Vector Machine Lyapunov Exponent Chaotic System Correlation Dimension Strange Attractor 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jiancheng Sun
    • 1
  • Lun Yu
    • 1
  • Guang Yang
    • 2
  • Congde Lu
    • 3
  1. 1.College of Physics and Information EngineeringFuzhou UniversityFuzhouChina
  2. 2.Department of communication EngineeringXi’an Institute of Posts and TelecommunicationsXi’anChina
  3. 3.Department of Information and Communication EngineeringXi’an Jiaotong UniversityXi’anChina

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