CLASSIFICATION OF HIGHLY NOISY SIGNALS USING GLOBAL DYNAMICAL MODELS

We present a classification scheme for time series with nonlinear corr elations, using global models of chaotic dynamical systems theory. We demonstrate classification in high-noise regimes, and argue that class ification probabilities can be directly computed from ensemble statist ics in the model coefficient space. We also develop a modification for nonstationary signals.