Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model
Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model
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DOI：
10.1007/s11771-014-2077-2
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发表时间：
2014-04
影响因子：
4.4
通讯作者：
Qi-jie Wang;Yanyan Du;Jian Liu
中科院分区：
文献类型：
--
作者：
Qi-jie Wang;Yanyan Du;Jian Liu
针对日长变化具有复杂时变特性的特点，提出了广义回归神经网络（GRNN）模型。同时，考虑到轴向大气角动量（AAM）函数与LOD变化密切相关，将其引入GRNN预测模型，进一步提高预测精度。利用LOD变化的观测数据进行的实验表明，GRNN模型的预测精度比BP网络提高了6.1%，引入AAM函数后，预测精度的提高进一步提高到14.7%。结果表明，结合AAM函数的GRNN是一种有效的LOD变化预测方法。
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.