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Proceedings Paper

Evolutionary program for the identification of dynamical systems
Author(s): Peter J. Angeline; David B. Fogel
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Paper Abstract

Various forms of neural networks have been applied to identification of non-linear dynamical systems. In most of these methods, the network architecture is set prior to training. In this paper, a method that evolves a symbolic solution for plant models is described. This method uses a evolutionary program to manipulate collections of parse trees expressed in a task specific language. Experiments performed on two unknown plants show this method is competitive with those that train neural networks for similar problems.

Paper Details

Date Published: 4 April 1997
PDF: 9 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271503
Show Author Affiliations
Peter J. Angeline, Lockheed Martin Federal Systems (United States)
David B. Fogel, Natural Selection, Inc. (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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