【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 .更多还原

【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.更多还原