Reconstructed dynamics and chaotic signal modeling

A nonlinear AR model is derived from the reconstructed dynamics of a signal. The underlying system is assumed to be nonlinear, autonomous, and deterministic. In this formulation. The output error scheme is shown to be more suitable than the equation error scheme in network training. A method to incorporate the information of dynamical invariants in signal modeling is proposed. Using this global information, the authors are able to avoid the oscillation problem in training a network to model chaotic time series.<<ETX>>

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