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.
出处
《天文学报》
CSCD
北大核心
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
