Robust Learning of Chaotic Attractors

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Rembrandt Bakker, Jaap Schouten, Marc-Olivier Coppens, Floris Takens, C. Giles, Cor van den Bleek


A fundamental problem with the modeling of chaotic time series data is that minimizing short-term prediction errors does not guarantee a match between the reconstructed attractors of model and experiments. We introduce a modeling paradigm that simultaneously learns to short-tenn predict and to locate the outlines of the attractor by a new way of nonlinear principal component analysis. Closed-loop predictions are constrained to stay within these outlines, to prevent divergence from the attractor. Learning is exceptionally fast: parameter estimation for the 1000 sample laser data from the 1991 Santa Fe time series competition took less than a minute on a 166 MHz Pentium PC.