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Optical Engineering

BaTiO3-based optical quadratic neural network implementing the perceptron algorithm
Author(s): Alex V. Huynh; John F. Walkup; Thomas F. Krile
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Paper Abstract

An optical quadratic neural network utilizing four-wave mixing in barium titanate (BaTiO3) has been developed. This network implements a feedback loop using a CCD camera, a microcomputer, two monochrome liquid crystal televisions, and various optical elements. For training, the network employs the supervised quadratic perceptron algorithm to associate binary-valued input vectors with specified training vectors. Using a spatial multiplexing scheme for two bipolar neurons, the quadratic network was able to associate an input vector with various target vectors. In addition, the network successfully associated two input vectors with two corresponding target vectors in the same training session. Both analytical and experimental results are presented.

Paper Details

Date Published: 1 May 1992
PDF: 7 pages
Opt. Eng. 31(5) doi: 10.1117/12.56163
Published in: Optical Engineering Volume 31, Issue 5
Show Author Affiliations
Alex V. Huynh
John F. Walkup, NASA Ames Research Ctr. (United States)
Thomas F. Krile, Texas Tech Univ. (United States)


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