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Use of neural networks to predict the short-term behavior of chaotic time series, including effects of superimposed noise

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3 Author(s)
G. H. Brawley ; Dept. of Eng. Mech., Battelle Memorial Inst., Columbus, OH, USA ; A. J. Markworth ; P. Parmananda

The predictive capabilities of some simple backpropagation neural networks, as applied to chaotic time series, are investigated using time-series data generated from a three-dimensional numerical model of an electrochemical system. Regulated amounts of noise are superimposed on the originally “clean” chaotic data in order that effects of noise on predictive capabilities can be evaluated. The ability of the neural networks to make short-term predictions of time-series behavior is assessed in terms of network size, extent ahead in time of the prediction, and level of superimposed noise

Published in:

System Theory, 1994., Proceedings of the 26th Southeastern Symposium on

Date of Conference:

20-22 Mar 1994