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Evolving recurrent perceptrons for time-series modeling.

J R McDonnell¹, D Waagen

¹RDT and E Div., NCCOSC, San Diego, CA.

IEEE Transactions on Neural Networks

|January 1, 1994

Summary

This study introduces a hybrid optimization method combining evolutionary programming and Solis-Wets techniques to generate recurrent perceptrons for time-series modeling. The approach effectively determines model order and coefficients for nonlinear IIR filters.

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Area of Science:

Computational intelligence
Machine learning
Signal processing

Background:

Recurrent perceptrons are nonlinear Infinite Impulse Response (IIR) filters.
Evolutionary programming is a multi-agent stochastic search technique.
Optimizing recurrent perceptrons requires advanced search strategies.

Purpose of the Study:

To develop a hybrid optimization scheme for generating recurrent perceptrons.
To investigate the performance of the proposed hybrid stochastic search method.
To apply the method for determining model order and coefficients of recurrent perceptron time-series models.

Main Methods:

A hybrid optimization scheme embedding the Solis-Wets single-agent stochastic search into evolutionary programming.
Augmenting the hybrid approach with 'blending' of randomly selected parent vectors for offspring generation.
Utilizing an information criterion to evaluate recurrent perceptron structures.

Main Results:

The hybrid stochastic search method demonstrated effective performance on benchmark response surfaces (Bohachevsky and Rosenbrock).
The approach successfully determined both model order and coefficients for recurrent perceptron time-series models.
The proposed method provides a robust framework for optimizing complex filter structures.

Conclusions:

The hybrid optimization approach is effective for generating and optimizing recurrent perceptrons.
The stochastic training method can be extended to radically recurrent perceptron architectures.
This research contributes to advanced time-series modeling using intelligent computational techniques.