Feature-enhanced optical interpattern-associative neural network model and its optical implementation

Chunfei Li; Wenlu Wang; Shutian Liu; Jie Wu

doi:10.1117/12.131210

30 October 1992 Feature-enhanced optical interpattern-associative neural network model and its optical implementation

Chunfei Li, Wenlu Wang, Shutian Liu, Jie Wu

Proceedings Volume 1812, Optical Computing and Neural Networks; (1992) https://doi.org/10.1117/12.131210
Event: International Symposium on Optoelectronics in Computers, Communications, and Control, 1992, Hsinchu, Taiwan

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.

Citation Download Citation

Chunfei Li, Wenlu Wang, Shutian Liu, and Jie Wu "Feature-enhanced optical interpattern-associative neural network model and its optical implementation", Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); https://doi.org/10.1117/12.131210

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