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Proceedings Paper

Feature-enhanced optical interpattern-associative neural network model and its optical implementation
Author(s): Chunfei Li; Wenlu Wang; Shutian Liu; Jie Wu
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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 hence, it is easier for the thresholding performance and increases 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: 30 October 1992
PDF: 7 pages
Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); doi: 10.1117/12.131210
Show Author Affiliations
Chunfei Li, Harbin Institute of Technology (China)
Wenlu Wang, Harbin Institute of Technology (United States)
Shutian Liu, Harbin Institute of Technology (China)
Jie Wu, Harbin Institute of Technology (China)

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

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