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Application of neural nets to system identification and bifurcation analysis of real world experimental data [electronic resource].

Published
Washington, D.C. : United States. Dept. of Defense, 1990.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
Pages: (13 pages) : digital, PDF file
Additional Creators
Los Alamos National Laboratory, United States. Department of Defense, National Science Foundation (U.S.), and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
We report results on the use of neural nets, and the closely related radial basis nets'', to analyze experimental time series from electro-chemical systems. We show how the nets may be used to derive a map that describes the nonlinear system, and how reserving an extra input line'' of the network allows one to learn the system behavior dependent on a control variable. Pruning'' of the network after training appears to result in elimination of spurious connection weights and enhanced predictive accuracy. Subsequent analysis of the learned map using techniques of bifurcation theory allows both nonlinear system identification and accurate and efficient predictions of long-term system behavior. The electrochemical system that was used involved the electrodissolution of copper in phosphoric acid. This system exhibits interesting low dimensional dynamics such transitions from steady state to oscillatory behavior and from period-one to period-two oscillations. This analysis provides an example of methodology that can be fruitful in understanding systems for which no adequate phenomenological model exists, or for which predictions of system behavior given a large scale, complicated model is inherently impractical. 17 refs., 2 figs.
Report Numbers
E 1.99:la-ur-90-515
E 1.99: conf-900398--1
conf-900398--1
la-ur-90-515
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Other Subject(s)
Note
Published through SciTech Connect.
02/01/1990.
"la-ur-90-515"
" conf-900398--1"
"DE90007539"
1990 international conference on neural networks, Lyons (France), Mar 1990.
Hudson, J.L.; Farber, R.M.; Lapedes, A.S.; Kevrekidis, I.G. . Dept. of Chemical Eng; Adomaitis, R.A.; Kube, M. . Dept. of Chemical Engineering.
PFF
VACIT
Funding Information
W-7405-ENG-36
View MARC record | catkey: 14079665