BaTiO3-based optical quadratic neural network implementing the perceptron algorithm

Alex V. Huynh; John F. Walkup; Thomas F. Krile

doi:10.1117/12.56163

1 May 1992 BaTiO₃-based optical quadratic neural network implementing the perceptron algorithm

Alex V. Huynh, John F. Walkup, Thomas F. Krile

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Optical Engineering, Vol. 31, Issue 5, (May 1992). https://doi.org/10.1117/12.56163

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.

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Alex V. Huynh, John F. Walkup, and Thomas F. Krile "BaTiO₃-based optical quadratic neural network implementing the perceptron algorithm," Optical Engineering 31(5), (1 May 1992). https://doi.org/10.1117/12.56163

Published: 1 May 1992

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