Evolving Recurrent Perceptrons
NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA
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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.
- Operations Research