| 基于广义回归神经网络的GPS高程转换 |
| |
| 引用本文： | 王新志,祝明坤,曹爽.基于广义回归神经网络的GPS高程转换[J].大地测量与地球动力学,2011,31(6):113-116. |
| |
| 作者姓名： | 王新志 祝明坤 曹爽 |
| |
| 作者单位： | 1. 南京信息工程大学遥感学院,南京,2100441
2. 青岛勘察测绘研究院,青岛,266032 |
| |
| 摘 要： | 为提高GPS高程转换的精度,采用广义回归神经网络(GRNN)进行拟合.将控制点的X、y坐标作为网络输入,高程异常作为网络输出,采用实验数据训练网络,训练完成的网络作为模型进行高程异常预测.结果表明,GRNN方法具有较高的GPS转换精度.
|
| 关 键 词： | 广义回归神经网络 BP神经网络 大地高 高程异常 正常高 |
TRANSFORMATION OF GPS HEIGHT BASED ON GENERAL REGRESSION NEURAL NETWORK |
| |
| Wang Xinzhi,Zhu Mingkun,Cao Shuang.TRANSFORMATION OF GPS HEIGHT BASED ON GENERAL REGRESSION NEURAL NETWORK[J].Journal of Geodesy and Geodynamics,2011,31(6):113-116. |
| |
| Authors: | Wang Xinzhi Zhu Mingkun Cao Shuang |
| |
| Institution: | 1) 1)School of Remote Sensing,Nanjing University of Information Science and Technology,Nanjing 210044 2)Qingdao Institute of Surveying Mapping and Geotechnical Investigation,Qingdao 260032 |
| |
| Abstract: | To improve the accuracy of GPS height transform from geodetic height to normal height, General Regression Neural Network（GRNN）was used for fitting. The X and Y coordinates of the control points were employed as the inputs of GRNN, and the elevation anomaly were the outputs of the neural network.We adopted experimental data for training the network, then, took the trained network as a model to complete the abnormal height prediction. The results show that the GRNN method is feasible and has the high accuracy of the GPS height transform. |
| |
| Keywords: | general regression neural network BP neural network geodetic height elevation anomaly normal height |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《大地测量与地球动力学》浏览原始摘要信息 |
| 点击此处可从《大地测量与地球动力学》下载免费的PDF全文 |
