The N-N-N Conjecture In Art1

    Authors

    M. Georgiopoulos; G. L. Heileman;J. X. Huang

    Comments

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    Abbreviated Journal Title

    Neural Netw.

    Keywords

    NEURAL NETWORK; PATTERN RECOGNITION; SELF-ORGANIZATION; LEARNING; ADAPTIVE RESONANCE THEORY; ART1; Computer Science, Artificial Intelligence

    Abstract

    In this paper we consider the ART1 neural network architecture introduced by Carpenter and Grossberg. In their original paper, Carpenter and Grossberg made the following conjecture: In the fast learning case, if the F2 layer in ART1 has at least N nodes, then each member of a list of N input patterns presented cyclically at the F1 layer of ART1 will have direct access to an F2 layer node after at most N list presentations. In this paper, we demonstrate that the conjecture is not valid for certain large L values, where L is a network parameter associated with the adaptation of the bottom-up traces in ART1. It is worth noting that previous work has shown the conjecture to be true for small L values.

    Journal Title

    Neural Networks

    Volume

    5

    Issue/Number

    5

    Publication Date

    1-1-1992

    Document Type

    Article

    Language

    English

    First Page

    745

    Last Page

    753

    WOS Identifier

    WOS:A1992JL88500002

    ISSN

    0893-6080

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