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

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.KeywordsSupport Vector MachineLyapunov ExponentChaotic SystemCorrelation DimensionStrange AttractorThese 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.