Modelling of chaotic systems with recurrent least squares support vector machines combined with stationary wavelet transform

Authors:
Jiancheng Sun;Lun Yu;Guang Yang;Congde Lu
Affiliations:
College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujiang, China;College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujiang, China;Department of communication Engineering, Xi'an Institute of Posts and Telecommunications, Xi'an, Shaanxi, China;Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

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.