Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model
来源期刊：中南大学学报(英文版)2014年第4期
论文作者：WANG Qi-jie(王琪洁) DU Ya-nan(杜亚男) LIU Jian(刘建)
文章页码：1396 - 1401
Key words：general regression neural network (GRNN); length of day; atmospheric angular momentum (AAM) function; prediction
Abstract: 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.
WANG Qi-jie(王琪洁), DU Ya-nan(杜亚男), LIU Jian(刘建)
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)
Abstract: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.
Key words:general regression neural network (GRNN); length of day; atmospheric angular momentum (AAM) function; prediction
