Modelling of Chaotic Systems with Recurrent Least Squares Support Vector Machines Combined with Reconstructed Embedding Phase Space - Xiang, Zheng; Zhang, Taiyi; Sun, Jiancheng (Advances in Natural Computation, 2005)
A new strategy of modelling of chaotic systems is presented. First, more information is acquired utilizing the reconstructed embedding phase space. Then, based on the Recurrent Least Squares Support Vector Machines (RLS-SVM), modelling of the chaotic system is realized. We use the power spectrum and dynamic invariants involving the Lyapunov exponents and the correlation dimension as criterions, and then apply our method to the Chua‘s circuit time series. The simulation 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.