Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Optical Matrix-Vector Implementation of Binary Valued Backpropagation

Not Accessible

Your library or personal account may give you access

Abstract

Optical implementations of neural networks can combine advantages of neural network adaptive parallel processing and optical free-space connectivity. Binary valued Backpropagation1, a supervised learning algorithm related to standard Backpropagation2, significantly reduces interconnection storage and computation requirements. This implementation of binary valued Backpropagation used optical matrix-vector multiplication3 to represent the forward information flow between network layers. Previous analog optical network memory systems have been described4.

© 1991 Optical Society of America

PDF Article
More Like This
Experimental comparison of different associative memory techniques implemented optically by the same system architecture

K.J. Weible, N. Collings, W. Xue, G. Pedrini, and R. Dändliker
ME9 Optical Computing (IP) 1991

Effects of Imperfection in Spatial Optical Devices on Backpropagation Learning Capability of Optoelectronic Neural Network

Satoshi Ishihara, Nobuyuki Kasama, Masahiko Mori, Yoshio Hayasaki, and Toyohiko Yatagai
PdP2 Optical Computing (IP) 1991

Optical perceptron-based quadratic neural network

Alex V. Huynh, John F. Walkup, and Thomas F. Krile
MII8 OSA Annual Meeting (FIO) 1991

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.