Abstract

Optoelectronic neural networks must not only be highly parallel but also fast to compete with electrical systems. Receiver noise becomes an important consideration at high data rates; so the limits set by noise to network size and speed are analyzed. A network incorporating an array of high-speed multi-quantum-well modulators was constructed. It employed a general method for optical representation of bipolar values, which required only a minimal increase in network dimensions and gave the network immunity to common-mode parameter variations. Different ways of partitioning pattern-recognition problems were compared, and the accuracy of one configuration was tested with the experimental network over a range of noise levels.

© 1995 Optical Society of America

Optoelectronic resonator neural networks

Yuri Owechko
Appl. Opt. 26(23) 5104-5111 (1987)

Design and analysis of fixed planar holographic interconnects for optical neural networks

Paul E. Keller and Arthur F. Gmitro
Appl. Opt. 31(26) 5517-5526 (1992)

Adaptive, optical, radial basis function neural network for handwritten digit recognition

Wesley E. Foor and Mark A. Neifeld
Appl. Opt. 34(32) 7545-7555 (1995)

Programmable optoelectronic neural network for optimization

Keith J. Symington, Yves Randle, Andrew J. Waddie, Mohammed R. Taghizadeh, and John F. Snowdon
Appl. Opt. 43(4) 866-876 (2004)

Neural network that incorporates direct optical imaging

Richard G. Stearns
Appl. Opt. 34(14) 2595-2604 (1995)