一次曲面模型和BP神经网络求高程异常的精度研究
surface model and BP neural network model analysis on
由于坐标系的不同，用GPS进行高程测量时必须首先进行高程拟合确定似大地水准面（即高程异常），这样才能将大地高转化为正常高。计算高程异常的方法很多，论文用一次曲面模型和BP神经网络的两种方法求高程异常，并对两种方法所求的高程异常进行精度分析，结果表明BP神经网络对求解高程异常的精度高，从而为BP神经网络在求高程异常中得到借鉴。
ue to the different coordinate system, Used GPS elevation measurements must be conducted first to determine elevation fitting quasi-geoid (ie height anomaly)，Only in this way can the earth into a normal high. The method of calculating the height anomaly are many，Thesis using a surface model and BP neural network to solve height anomaly，The request of the two methods for the accuracy of the height anomaly analysis，The results show that accuracy that the BP neural network to solve the height anomaly is the high ，neural network for BP measurement in the application of reference
王旭
测绘学地球物理学
高程异常一次曲面模型BP神经网络
height anomalya surface modelBP neural network
王旭.一次曲面模型和BP神经网络求高程异常的精度研究[EB/OL].(2009-04-23)[2025-08-11].http://www.paper.edu.cn/releasepaper/content/200904-744.点此复制
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