Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-1995

Abstract

This article introduces a neural architecture termed Adaptive Resonance Associative Map (ARAM) that extends unsupervised Adaptive Resonance Theory (ART) systems for rapid, yet stable, heteroassociative learning. ARAM can be visualized as two overlapping ART networks sharing a single category field. Although ARAM is simpler in architecture than another class of supervised ART models known as ARTMAP, it produces classification performance equivalent to that of ARTMAP. As ARAM network structure and operations are symmetrical, associative recall can be performed in both directions. With maximal vigilance settings, ARAM encodes pattern pairs explicitly as cognitive chunks and thus guarantees perfect storage and recall of an arbitrary number of arbitrary pattern pairs. Simulations on an iris plant and a sonar return recognition problems compare ARAM classification performance with that of counterpropagation network, K-nearest neighbor system, and back propagation network. Associative recall experiments on two pattern sets show that, besides the advantages of fast learning, guaranteed perfect storage, and full memory capacity, ARAM produces a stronger noise immunity than Bidirectional Associative Memory (BAM).

Keywords

Self-organization, Neural network architecture, Associative memory, Heteroassociative recall, Supervised learning

Discipline

Computer Engineering | Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Neural Networks

Volume

8

Issue

3

First Page

437

Last Page

446

ISSN

0893-6080

Identifier

10.1016/0893-6080(94)00092-Z

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/0893-6080(94)00092-Z

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