Evaluation of the use of the Hopfield neural network model as a nearest-neighbor algorithm

15 October 1986

journal article
Published by Optica Publishing Group in Applied Optics

Vol. 25 (20) , 3759-3766
https://doi.org/10.1364/ao.25.003759

Abstract

Neural network models are receiving increasing attention because of their collective computational capabilities. We evaluate the use of the Hopfield neural network model in optically determining the nearest-neighbor of a binary bipolar test vector from a set of binary bipolar reference vectors. The use of the Hopfield model is compared with that of a direct technique called direct storage nearest-neighbor that accomplishes the task of nearest-neighbor determination.

Keywords

This publication has 6 references indexed in Scilit:

Optical perfect shuffle
Applied Optics, 1986
Information capacity of the Hopfield model
IEEE Transactions on Information Theory, 1985
Optical implementation of the Hopfield model
Applied Optics, 1985
Optical information processing based on an associative-memory model of neural nets with thresholding and feedback
Optics Letters, 1985
The Converging Squares Algorithm: An Efficient Method for Locating Peaks in Multidimensions
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1984
Neural networks and physical systems with emergent collective computational abilities.
Proceedings of the National Academy of Sciences, 1982