The ART of adaptive pattern recognition by a self-organizing neuralnetwork
Carpenter, G.A.
Grossberg, S.
Center for Adaptive Syst., Boston Univ., MA;
This paper appears in: Computer
Publication Date: Mar 1988
Volume: 21,
Issue: 3
On page(s): 77-88
ISSN: 0018-9162
References Cited: 15
CODEN: CPTRB4
INSPEC Accession Number: 3142172
Digital Object Identifier: 10.1109/2.33
Current Version Published: 2002-08-06
Abstract
The adaptive resonance theory (ART) suggests a solution to the
stability-plasticity dilemma facing designers of learning systems,
namely how to design a learning system that will remain plastic, or
adaptive, in response to significant events and yet remain stable in
response to irrelevant events. ART architectures are discussed that are
neural networks that self-organize stable recognition codes in real time
in response to arbitrary sequences of input patterns. Within such an ART
architecture, the process of adaptive pattern recognition is a special
case of the more general cognitive process of hypothesis discovery,
testing, search, classification, and learning. This property opens up
the possibility of applying ART systems to more general problems of
adaptively processing large abstract information sources and databases.
The main computational properties of these ART architectures are
outlined and contrasted with those of alternative learning and
recognition systems
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