Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model  

作　　者：王琪洁 杜亚男 刘建 

机构地区：[1]School of Geosciences and Info-Physics, Central South University

出　　处：《Journal of Central South University》2014年第4期1396-1401,共6页中南大学学报（英文版）

基　　金：Projects(U1231105,10878026)supported by the National Natural Science Foundation of China

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.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.

关 键 词：general regression neural network（GRNN） length of day atmospheric angular momentum（AAM） function prediction 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]