<title>Feature-enhanced optical interpattern-associative neural network model and its optical implementation</title>

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.