Electrical & Computer Engineering Faculty Publications

Document Type

Article

Publication Date

11-1-1994

Abstract

A set of nonlinear differential equations that describe the dynamics of the ART1 model are presented, along with the motivation for their use. These equations are extensions of those developed by Carpenter and Grossberg (1987). It is shown how these differential equations allow the ART1 model to be realized as a collective nonlinear dynamical system. Specifically, we present an ART1-based neural network model whose description requires no external control features. That is, the dynamics of the model are completely determined by the set of coupled differential equations that comprise the model. It is shown analytically how the parameters of this model can be selected so as to guarantee a behavior equivalent to that of ART1 in both fast and slow learning scenarios. Simulations are performed in which the trajectories of node and weight activities are determined using numerical approximation techniques.

Publisher

IEEE

Publication Title

IEEE Transactions on Neural Networks

ISSN

1045-9227

Volume

5

Issue

6

First Page

873

Last Page

889

DOI

10.1109/72.329684

Language (ISO)

English

Sponsorship

IEEE

Keywords

Chaos, Computer architecture, Differential equations

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