Title

The N-N-N Conjecture In Art1

Keywords

Adaptive resonance theory; ART1; Learning; Neural network; Pattern recognition; Self-organization

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. © 1992 Pergamon Press Ltd.

Publication Date

1-1-1992

Publication Title

Neural Networks

Volume

5

Issue

5

Number of Pages

745-753

Document Type

Article

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/S0893-6080(05)80135-2

Socpus ID

0026923906 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0026923906

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