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 language | English (US) |
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Title of host publication | TENCON '89: Fourth IEEE Region 10 International Conference |
Place of Publication | Piscataway, NJ, United States |
Publisher | Publ by IEEE |
Pages | 462-466 |
Number of pages | 5 |
State | Published - 1989 |
Event | 4th IEEE Region 10th International Conference - TENCON '89 - Bombay, India Duration: Nov 22 1989 → Nov 24 1989 |
Other
Other | 4th IEEE Region 10th International Conference - TENCON '89 |
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City | Bombay, India |
Period | 11/22/89 → 11/24/89 |
ASJC Scopus subject areas
- General Engineering