The N-N-N Conjecture In Art1
Title - Alternative
NEURAL NETWORK; PATTERN RECOGNITION; SELF-ORGANIZATION; LEARNING; ADAPTIVE RESONANCE THEORY; ART1; Computer Science, Artificial Intelligence
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.
Georgiopoulos, M, "The N-N-N Conjecture In Art1" (1992). Faculty Bibliography. 2053.