Electrical & Computer Engineering Faculty Publications

Document Type

Article

Publication Date

3-29-1994

Abstract

This paper reviews a formalism that enables the dynamics of a broad class of neural networks to be understood. This formalism is then applied to a specific network and the predicted and simulated behavior of the system are compared. The purpose of this work is to utilise a model of the dynamics that also describes the phase space behavior and structural stability of the system. This is achieved by writing the general equations of the neural network dynamics as a gradient-like system. In this paper it is demonstrated that a network with additive activation dynamics and Hebbian weight update dynamics can be expressed as a gradient-like system. An example of an S-layer network with feedback between adjacent layers is presented. It is shown that the process of weight learning is stable in this network when the learned weights are symmetric. Furthermore, the weight learning process is stable when the learned weights are asymmetric, provided that the activation is computed using only the symmetric part of the weights.

Publisher

IEEE

Publication Title

Southcon/94. Conference Record

First Page

92

Last Page

96

DOI

10.1109/SOUTHC.1994.498081

Language (ISO)

English

Sponsorship

IEEE

Keywords

Application software, Computational modeling, Chaos

Download

Share

COinS