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Prediction of dynamical phenomena by a neural network
Grabec, Igor
The article describes an adaptive information processing system capable of predicting dynamical phenomena. It includes a neural network-like memory, a predictor, two shift registers, and a ...comparator. In the memory an internal empirical model of observed phenomenon is formed. It is described by a set of memorized prototype transitions between successive states of input time dependent signal which can also be chaotic. The adaptation of prototypes corresponds to a self-organization process which maximally preserves information obtained by measurements. In the predictor the forthcoming signal is estimated from the prototypes. The system operation is demonstrated on a chaotic signal generated by the Henon map. By minimizing the prediction error, determined by the comparator, a proper embedding dimension of the chaotic attractor can be selected, while the Lyapunov coefficient can be estimated from the discrepancy between the predicted and the input signal.
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