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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rao, A. Walker, M.R. Clark, L.T. Akers, L.A. |
| Copyright Year | 1989 |
| Description | Author affiliation: Center for Solid State Electron. Res., Arizona State Univ., Tempe, AZ, USA (Rao, A.; Walker, M.R.; Clark, L.T.; Akers, L.A.) |
| 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. |
| Starting Page | 462 |
| Ending Page | 466 |
| File Size | 449788 |
| Page Count | 5 |
| File Format | |
| DOI | 10.1109/TENCON.1989.176979 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1989-11-22 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Subspace constraints Adaptive filters Solid state circuits Resonance Clustering methods Brain modeling Humans Pattern recognition Deafness Hardware |
| Content Type | Text |
| Resource Type | Article |
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