广义回归神经网络在日长变化预报中的应用  

An Application to the Prediction of LOD Change Based on General Regression Neural Network

作　　者：张晓红[1] 王琪洁[1] 朱建军[1] 张昊[1] 

机构地区：[1]中南大学地球科学与信息物理学院,长沙410083

出　　处：《天文学报》2011年第4期322-331,共10页Acta Astronomica Sinica

基　　金：国家自然科学基金委员会与中国科学院天文联合基金项目(10878026)资助

摘　　要：传统的日长变化预报多是基于线性模型,如最小二乘模型、自回归模型等,但是日长变化包含了复杂的非线性因素,线性模型预报的效果往往不甚理想.所以尝试使用一种非线性神经网络—广义回归神经网络(GRNN)模型进行日长变化预报,并将结果与使用BP(Back Propagation)神经网络模型和其它模型的预报结果进行比较.结果表明,GRNN用于日长变化预报是高效可行的.Traditional prediction of the LOD （length of day） change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network --- general regression neural network （GRNN） model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

关 键 词：天体测量 时间 方法 其它诸多方面 

分 类 号：P128[天文地球—天体测量]