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Proceedings Paper

Application of interpattern association to gray-level neural net
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Paper Abstract

A gray level discrete associative memory (GLDAM) neural network using interpattern association (IPA) model is presented. By decomposing a gray level pattern into bipolar/binary modes of subpatterns, a GLDAM can be constructed. Although GLDAM improves the information capacity of the neural net, the decomposition process introduces sparse allocation in memory matrix, which affects the performance of the neural net. Computer simulated results for the Hopfield and the IPA models are provided, in which we have shown that the IPA GLDAM performs better.

Paper Details

Date Published: 30 October 1992
PDF: 12 pages
Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); doi: 10.1117/12.131221
Show Author Affiliations
Chii-Maw Uang, The Pennsylvania State Univ. (Taiwan)
Francis T. S. Yu, The Pennsylvania State Univ. (United States)

Published in SPIE Proceedings Vol. 1812:
Optical Computing and Neural Networks
Ken Yuh Hsu; Hua-Kuang Liu, Editor(s)

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