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 WANG Qi-jie;DU Ya-nan;LIU Jian

作者机构：School of Geosciences and Info-Physics Central South University 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2014年第21卷第4期

页      面：1396-1401页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0806[工学-冶金工程] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：神经网络模型 回归神经网络 预测模型 大气角动量 GRNN 广义 预测精度 LOD 

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.