Abstract

The content-addressable network (CAN) is an efficient, intrinsically discrete training algorithm for binary-valued classification networks. The binary nature of the CAN network permits accelerated learning and significantly reduced hardware-implementation requirements. A multilayer optoelectronic CAN network employing matrix–vector multiplication was constructed. The network learned and correctly classified trained patterns, gaining a measure of fault tolerance by learning associative solutions to optical hardware imperfections. Operation of this system is possible owing to the reduced hardware accuracy requirements of the CAN learning algorithm.

© 1993 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Influence of interconnection weight discretization and noise in an optoelectronic neural network

A. Von Lehmen, E. G. Paek, P. F. Liao, A. Marrakchi, and J. S. Patel
Opt. Lett. 14(17) 928-930 (1989)

Noise analysis of polarization-based optoelectronic connectionist machines

Michael G. Robinson and Kristina M. Johnson
Appl. Opt. 31(2) 263-272 (1992)

Optical neural networks: an introduction by the feature editors

Kelvin Wagner and Demetri Psaltis
Appl. Opt. 32(8) 1261-1263 (1993)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (9)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (8)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription