ART1 network implementation issues

Arun Rao, Mark R. Walker, L. T. Clark, L. A. Akers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Adaptive resonance theory (ART) is a neural-network based clustering method developed by G. A. Carpenter and S. Grossberg (1987). Its inspiration is neurobiological and its component parts are intended to model a variety of hierarchical inference levels in the human brain. Neural networks based upon ART are capable of 'recognizing' patterns close to previously stored patterns according to some criterion, and storing patterns which are not close to already stored patterns. Two varieties of ART networks have been proposed. ART1 recognizes binary inputs and ART2 can deal with general analog inputs as well. Since the emphasis of this work is on conventional hardware implementation, ART1 is mainly discussed.

Original languageEnglish (US)
Title of host publicationTENCON '89: Fourth IEEE Region 10 International Conference
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
Pages462-466
Number of pages5
StatePublished - 1989
Event4th IEEE Region 10th International Conference - TENCON '89 - Bombay, India
Duration: Nov 22 1989Nov 24 1989

Other

Other4th IEEE Region 10th International Conference - TENCON '89
CityBombay, India
Period11/22/8911/24/89

ASJC Scopus subject areas

  • Engineering(all)

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