DocumentCode :
1825955
Title :
Use of neural networks to predict the short-term behavior of chaotic time series, including effects of superimposed noise
Author :
Brawley, Gary H. ; Markworth, Alan J. ; Parmananda, Punit
Author_Institution :
Dept. of Eng. Mech., Battelle Memorial Inst., Columbus, OH, USA
fYear :
1994
fDate :
20-22 Mar 1994
Firstpage :
643
Lastpage :
649
Abstract :
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
Keywords :
backpropagation; chaos; neural nets; nonlinear systems; time series; backpropagation; chaotic time series; electrochemical system; network size; neural networks; short-term behavior; superimposed noise; three-dimensional numerical model; Backpropagation; Chaos; Equations; Neural networks; Noise generators; Noise level; Numerical models; Physics; Predictive models; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, 1994., Proceedings of the 26th Southeastern Symposium on
Conference_Location :
Athens, OH
ISSN :
0094-2898
Print_ISBN :
0-8186-5320-5
Type :
conf
DOI :
10.1109/SSST.1994.287798
Filename :
287798
Link To Document :
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