摘要
以二次多项式模型为基础,提出融合重力信息的多项式模型和融合重力信息的误差反向传播(error back propagation,BP)神经网络模型2种似大地水准面精化模型。通过试验验证,3种模型的检验点拟合中误差分别为±11.7 cm、±4.3 cm和±4.0 cm,重力信息可提高似大地水准面模型的拟合效果,BP神经网络模型的拟合精度最高,与二次多项式模型相比提高了65%。
Based on the quadratic polynomial model,two kinds of Quasi-geoid refinement models are proposed:a polynomial model fused with gravity information,and a BP neural network methodfused with gravity information.Through the test verification,the checkpoint fitting error of the three models is±11.7 cm,±4.3 cm and±4.0 cm respectively,the gravity information can improve the fitting effect of the Quasi-geoid model,and the fitting accuracy of the BP neural network model is the highest,which is 65%higher than that of the quadratic polynomial model.
作者
沈鑫
胡伍生
SHEN Xin;HU Wusheng(School of Transportation,Southeast University,Nanjing 211189,China)
出处
《山东交通学院学报》
CAS
2020年第4期71-78,共8页
Journal of Shandong Jiaotong University
基金
国家自然科学基金项目(41574022)。