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Proceedings Paper

Evolving predictors for chaotic time series
Author(s): Peter J. Angeline
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Paper Abstract

Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current method's performance is shown to compare favorably with using larger neural networks.

Paper Details

Date Published: 25 March 1998
PDF: 11 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304803
Show Author Affiliations
Peter J. Angeline, Natural Selection, Inc. (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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