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Cited by 1,287

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

The ART of adaptive pattern recognition by a self-organizing neural network

Carpenter, G.A., Grossberg, S.·1988·Computer
DOI 10.1109/2.33
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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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Metadata

Authors
Carpenter, G. A., & Grossberg, S.
Venue
Computer
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1988-03
Volume
21
Issue
3
Pages
77-88
ISSN
0018-9162 (ISSN-L)
DOI
10.1109/2.33
OpenAlex
W2106304233
Cited by
1,287
References
15
FWCI
43.49
Type
journal article
Language
en
Open access
closed

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