A Dynamical Adaptive Resonance Architecture

    Authors

    G. L. Heileman; M. Georgiopoulos;C. Abdallah

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Neural Netw.

    Keywords

    RECOGNITION; NETWORKS; ART1; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

    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 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.

    Journal Title

    Ieee Transactions on Neural Networks

    Volume

    5

    Issue/Number

    6

    Publication Date

    1-1-1994

    Document Type

    Article

    Language

    English

    First Page

    873

    Last Page

    889

    WOS Identifier

    WOS:A1994PQ76300002

    ISSN

    1045-9227

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