Evolving recurrent perceptrons

John R. McDonnell; Donald E. Waagen

doi:10.1117/12.152634

19 August 1993 Evolving recurrent perceptrons

John R. McDonnell, Donald E. Waagen

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152634
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

John R. McDonnell and Donald E. Waagen "Evolving recurrent perceptrons", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152634

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