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 perfor- mance with that of counterpropagation network, K-nearest neighbor system, and back-propagation network. Asso- ciative 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 Mem- ory ( BAM ).
['Ah-Hwee Tan']
Adaptive resonance associative map