BaTiO3-based optical quadratic neural network implementing the perceptron algorithm

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 s...

Full description

Saved in:
Bibliographic Details
Published in:Optical Engineering 1992-05, Vol.31 (5), p.979-985
Main Authors: Huynh, Alex V, Walkup, John F, Krile, Thomas F
Format: Article
Language:English
Subjects:
Applied sciences
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Neural networks
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. (Author)
ISSN:0091-3286
1560-2303
DOI:10.1117/12.56163