Abstract: In the modeling process of Artificial Neural Network (ANN),an appropriate partition of train samples and test samples conduces to a high modeling efficiency.In most cases,test samples are selected randomly.In this paper,a Euclidian distance based self-clustering algorithm is proposed to partition train samples and test samples automatically.All the samples are attributed to different clusters firstly; each individual sample is affiliated to the nearest cluster according to the Euclidian distance from the kernels of other clusters whereafter; and then small clusters are divided up into others;at last test samples are selected from each cluster in the same proportion.Two case studies show that this algorithm leads to a better test result in comparison with the random method,and therefore increases the modeling efficiency.

摘要： 建立神经网络模型时 ,能否合理地划分训练样本和检验样本直接关系到建模的效率 .在很多实际应用中 ,检验样本是随机抽取的 .本文提出了一种基于欧氏距离的自聚类算法 ,根据样本的空间分布情况对其自动分类 ,然后确定检验样本 .算例研究表明 ,应用此算法能够改善检验效果 ,从而提高建模效率 .