中文摘要：

一般回归神经网络(GRNN ) 模型被建议为白天(LOD ) 的长度建模并且预言变化，它有很复杂的变化时间的特征。同时，考虑到轴的大气的尖动量(AAM ) 功能紧与 LOD 被相关，这变化，进一步改进预言的精确性被介绍进 GRNN 预言模型。有 GRNN 模型的预言精确性比 BP 的高是 6.1% 的 LOD 变化表演的观察数据的实验联网，并且在介绍 AAM 以后，工作预言精确性的改进进一步增加到 14.7% 。结果证明有 AAM 功能的 GRNN 是为 LOD 变化的一个有效预言方法。

英文摘要：

The general regression neural network（GRNN） model was proposed to model and predict the length of day（LOD） change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum（AAM） function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.