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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/276867 |
| Date | January 1988 |
| Creators | Feinleib, Richard Eric, 1964- |
| Contributors | Gibbs, Hyatt M. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Thesis-Reproduction (electronic) |
| 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. |
Page generated in 0.0024 seconds