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

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.