Feature-enhanced optical interpattern associative neural network

Shutian Liu; Wenlu Wang; Ruibo Wang; Jie Wu; Chunfei Li

doi:10.1117/12.983194

2 February 1993 Feature-enhanced optical interpattern associative neural network

Shutian Liu, Wenlu Wang, Ruibo Wang, Jie Wu, Chunfei Li

Proceedings Volume 1773, Photonics for Computers, Neural Networks, and Memories; (1993) https://doi.org/10.1117/12.983194
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Shutian Liu, Wenlu Wang, Ruibo Wang, Jie Wu, and Chunfei Li "Feature-enhanced optical interpattern associative neural network", Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); https://doi.org/10.1117/12.983194

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