Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning


Show simple item record Tan, Ah-Hwee en_US 2011-11-14T18:24:28Z 2011-11-14T18:24:28Z 1992-02 en_US
dc.description.abstract This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions. en_US
dc.description.sponsorship Air Force Office of Scientific Research (90-0128) en_US
dc.language.iso en_US en_US
dc.publisher Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems en_US
dc.relation.ispartofseries BU CAS/CNS Technical Reports;CAS/CNS-TR-1992-016 en_US
dc.rights Copyright 1992 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. en_US
dc.title Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US

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