三种高程异常拟合模型比较  

Comparison of 3 Kinds of the GPS Height Anomaly Fitting

作　　者：邹明普[1] 叶芬[1] 

机构地区：[1]湖南省第一测绘院,湖南衡阳421001

出　　处：《北京测绘》2014年第6期20-22,共3页Beijing Surveying and Mapping

摘　　要：将二次曲面、BP神经网络、最小二乘支持向量机应用与高程异常拟合,并用某地区数据进行了实验验证,结果表明,最小二乘支持向量机应用于高程异常拟合精度最优。The quadraftc surface model BP neural network model and least squares support vector machine （LS-SVM） model is applied to the GPS height anomaly fitting. The GPS elevation data in a certain area is used, the results shows LS- SVM model would be significantly better than quadratic surface model and BP neural network model.

关 键 词：高程异常 二次曲面 BP神经网络 最小二乘支持向量机 

分 类 号：P228.4[天文地球—大地测量学与测量工程]