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Proceedings Paper

Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers
Author(s): Ching S. Ho; Juin J. Liou; Michael Georgiopoulos; Christos G. Christodoulou
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Paper Abstract

This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169983
Show Author Affiliations
Ching S. Ho, Univ. of Central Florida (United States)
Juin J. Liou, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)
Christos G. Christodoulou, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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