Evaluation of the use of the Hopfield neural network model as a nearest-neighbor algorithm.
Evaluation of the use of the Hopfield neural network model as a nearest-neighbor algorithm.
复制标题
使用 Hopfield 神经网络模型作为最近邻算法的评估。
DOI：
10.1364/ao.25.003759
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发表时间：
1986
期刊：
影响因子：
1.9
通讯作者：
B. Kumar
中科院分区：
文献类型：
--
作者：
B. L. Montgomery;B. Kumar
神经网络模型由于其集体计算能力而受到越来越多的关注。我们评估使用的Hopfield神经网络模型在光学上确定最近的邻居的一个二进制双极测试向量从一组二进制双极参考向量。Hopfield模型的使用相比，一个直接的技术称为直接存储最近邻，完成任务的最近邻确定。
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.