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 ArticleMore Like This
S. Abramson, D. Saad, E. Marom, and N. Konforti
Appl. Opt. 32(8) 1330-1337 (1993)
Stuart A. Collins, Stanley C. Ahalt, Ashok K. Krishnamurthy, and Daniel F. Stewart
Appl. Opt. 32(8) 1297-1303 (1993)
Mark A. Neifeld and Demetri Psaltis
Appl. Opt. 32(8) 1370-1379 (1993)