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Proceedings Paper

Integer-encoded massively parallel processing of fast-learning fuzzy ARTMAP neural networks
Author(s): Hubert A. Bahr; Ronald F. DeMara; Michael Georgiopoulos
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Paper Abstract

In this paper we develop techniques that are suitable for the parallel implementation of Fuzzy ARTMAP networks. Speedup and learning performance results are provided for execution on a DECmpp/Sx-1208 parallel processor consisting of a DEC RISC Workstation Front-End and MasPar MP-1 Back-End with 8,192 processors. Experiments of the parallel implementation were conducted on the Letters benchmark database developed by Frey and Slate. The results indicate a speedup on the order of 1000-fold which allows combined training and testing time of under four minutes.

Paper Details

Date Published: 4 April 1997
PDF: 12 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271530
Show Author Affiliations
Hubert A. Bahr, HQ STRICOM (United States)
Ronald F. DeMara, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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