Persistent Link:
http://hdl.handle.net/10150/276867
Title:
Adaptive optical learning network with a photorefractive crystal
Author:
Feinleib, Richard Eric, 1964-
Issue Date:
1988
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An optical computer which performs the classification of an input object pattern into one of two learned classes is designed and demonstrated. The classifier is an optical implementation of a neural network model of computation featuring learning, self-organization, and decision-making competition. Neural computation is discussed including models for learning networks and motivation for optical implementation. A discussion of photorefractive crystal holographic storage and adaptation is presented followed by experimental results of writing and erasing gratings in several different crystals. The optical network features a photorefractive crystal to store holographic interconnection weights and an opto-electronic circuit to provide a means of competitive decision making and feedback. Results of the optical learning network and its operation as an associative memory are followed by extensions of the architecture to allow improved performance and greater flexibility.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Neural networks (Computer science); Photorefractive materials.; Computers, Optical.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Optical Sciences
Degree Grantor:
University of Arizona
Advisor:
Gibbs, Hyatt M.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAdaptive optical learning network with a photorefractive crystalen_US
dc.creatorFeinleib, Richard Eric, 1964-en_US
dc.contributor.authorFeinleib, Richard Eric, 1964-en_US
dc.date.issued1988en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn optical computer which performs the classification of an input object pattern into one of two learned classes is designed and demonstrated. The classifier is an optical implementation of a neural network model of computation featuring learning, self-organization, and decision-making competition. Neural computation is discussed including models for learning networks and motivation for optical implementation. A discussion of photorefractive crystal holographic storage and adaptation is presented followed by experimental results of writing and erasing gratings in several different crystals. The optical network features a photorefractive crystal to store holographic interconnection weights and an opto-electronic circuit to provide a means of competitive decision making and feedback. Results of the optical learning network and its operation as an associative memory are followed by extensions of the architecture to allow improved performance and greater flexibility.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectPhotorefractive materials.en_US
dc.subjectComputers, Optical.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGibbs, Hyatt M.en_US
dc.identifier.proquest1335682en_US
dc.identifier.oclc22499582en_US
dc.identifier.bibrecord.b17444470en_US
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