Accession Number:

ADA273241

Title:

Evolving Recurrent Perceptrons

Descriptive Note:

Professional paper

Corporate Author:

NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA

Personal Author(s):

Report Date:

1993-10-01

Pagination or Media Count:

14.0

Abstract:

This work investigates the application of evolutionary programming, a multi-agent stochastic search technique, to the generation of recurrent perceptions nonlinear IIR 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 next-step predictors. The AIC is used as the cost function to score each candidate solution. Neural Networks, Evolutionary Programming, Signal Detection.

Subject Categories:

  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE