Prediction of chaotic time series with NARX recurrent dynamic neural networks

Authors:
Eugen Diaconescu
Affiliations:
Electronics, Communications and Computer Science Faculty, University of Pitesti, Romania
Venue:
ICAI'08 Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information
Year:
2008

Citing 4
Cited 0

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Abstract

The problem of chaotic time series prediction is studied in various disciplines now including engineering, medical and econometric applications. Chaotic time series are the output of a deterministic system with positive Liapunov exponent. A time series prediction is a suitable application for a neuronal network predictor. The NN approach to time series prediction is non-parametric, in the sense that it is not necessary to know any information regarding the process that generates the signal. It is shown that the recurrent NN (RNN) with a sufficiently large number of neurons is a realization of the nonlinear ARMA (NARMA) process. In this paper we present the nonlinear autoregressive network with exogenous inputs (NARX), the architecture, the training method, the input data to network, the simulation results.