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Proceedings Paper

Analog circuit design and implementation of an adaptive resonance theory (ART) neural network architecture
Author(s): Ching S. Ho; Juin J. Liou; Michael Georgiopoulos; Gregory L. Heileman; Christos G. Christodoulou
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Paper Abstract

This paper presents an analog circuit implementation for an adaptive resonance theory neural network architecture, called the augmented ART-1 neural network (AART1-NN). The AART1-NN is a modification of the popular ART1-NN, developed by Carpenter and Grossberg, and it exhibits the same behavior as the ART1-NN. The AART1-NN is a real-time model, and has the ability to classify an arbitrary set of binary input patterns into different clusters. The design of the AART1-NN model. The circuit is implemented by utilizing analog electronic components, such as, operational amplifiers, transistors, capacitors, and resistors. The implemented circuit is verified using the PSpice circuit simulator, running on Sun workstations. Results obtained from the PSpice circuit simulation compare favorably with simulation results produced by solving the differential equations numerically. The prototype system developed here can be used as a building block for larger AART1-NN architectures, as well as for other types of ART architectures that involve the AART1-NN model.

Paper Details

Date Published: 2 September 1993
PDF: 12 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152540
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)
Gregory L. Heileman, Univ. of New Mexico (United States)
Christos G. Christodoulou, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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