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Proceedings Paper

Feature-enhanced optical interpattern associative neural network
Author(s): Shutian Liu; Wenlu Wang; Ruibo Wang; Jie Wu; Chunfei Li
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Paper Abstract

In this paper we propose a Feature Enhanced Interpattern Associative (FEIPA) optical neural network. The common part of the stored patterns is regarded as redundance and its contribution in the association process is discarded. Therefore, the output before thresholding is more uniform, and it is more easier for the thresholding performance and increase the iteration speed. Furthermore, the optical implementation is much easier because all the elements of the interconnection matrix are non-negative and unipolar. The theoretical description and the experimental results are presented.

Paper Details

Date Published: 2 February 1993
Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); doi: 10.1117/12.983194
Show Author Affiliations
Shutian Liu, Harbin Institute of Technology (China)
Wenlu Wang, Harbin Institute of Technology (United States)
Ruibo Wang, Harbin Institute of Technology (United States)
Jie Wu, Harbin Institute of Technology (China)
Chunfei Li, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 1773:
Photonics for Computers, Neural Networks, and Memories
Stephen T. Kowel; John A. Neff; William J. Miceli, Editor(s)

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