Data di Pubblicazione:
1996
Citazione:
Experimental neural networks for prediction and identification / C. Alippi, V. Piuri. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - 45:2(1996), pp. 670-676. [10.1109/19.492807]
Abstract:
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 modelled is intrinsically nonlinear, its performance approaches that obtainable with classical linear identification. The work extends that suggested by Narendra in (1990) by considering a reduced set of training data and a black-box model for the system to be identified.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Identification ; Learning (artificial intelligence) ; Nonlinear dynamical systems ; Prediction theory ; Recurrent neural nets ; Time series.
Elenco autori:
C. Alippi, V. Piuri
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