Experimental neural networks for prediction and identification

In this paper we prove the effectiveness of using simple NARX-type (Nonlinear Auto-Regressive model with eXogenous variables) recurrent neural networks to identify time series and nonlinear dynamical systems. Experimentally we show that, whenever the process generating the data is ruled by a linear model, the performances provided by the neural network are comparable with the ones given by the optimal predictor determined according to the Kolmogorov-Wiener theory, On the other hand, whenever the system to be modeled is intrinsically nonlinear, its performance approaches that obtainable with classical linear identification, The work extends that suggested by Narendra in [1] by considering a reduced set of training data and a black-box model for the system to be identified.