A Dynamical Adaptive Resonance Architecture

Gregory L. Heileman, Michael Georgiopoulos, Chaouki Abdallah

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 [1], It is shown how these differential equations allow the ART1 model to be realized as a collective nonlinear dynamical system. Specifically, we present an ART 1-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.

Original languageEnglish (US)
Pages (from-to)873-889
Number of pages17
JournalIEEE Transactions on Neural Networks
Volume5
Issue number6
DOIs
StatePublished - Nov 1994
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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