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Proceedings Paper

Evolving recurrent perceptrons
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Paper Abstract

This woit investigates the application of evolutionary programming, a multi-agent stochastic search technique, to the generation of recurrent perceptrons (nonlinear hR filters) for time-series prediction tasks. The evolutionary programming paradigm is discussed and analogies are made to classical stochastic optimization methods. A hybrid optimization scheme is proposed based on multi-agent and single-agent random optimization techniques. This method is then used to determine both the model order and weight coefficients of linear, nonlinear, and parallel linear-nonlinear nextstep predictors. The AIC is used as the cost function to score each candidate solution.

Paper Details

Date Published: 19 August 1993
PDF: 12 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152634
Show Author Affiliations
John R. McDonnell, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Donald E. Waagen, Naval Command, Control and Ocean Surveillance Ctr. (United States)


Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

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