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.

© 1986 Optical Society of America

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