TRANSFORMATION OF GPS HEIGHT BASED ONGENERAL REGRESSION NEURAL NETWORK
Wang Xinzhi 1) ; Zhu Mingkun 2) ; and Cao Shuang 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.
Key words ：
general regression neural network
BP neural network
geodetic height
elevation anomaly
normal height
Received: 01 January 1900
Corresponding Authors:
Wang Xinzhi
Cite this article:
Wang Xinzhi ,Zhu Mingkun ,and Cao Shuang . TRANSFORMATION OF GPS HEIGHT BASED ONGENERAL REGRESSION NEURAL NETWORK[J]. , 2011, 31(6): 113-116.
Wang Xinzhi ,Zhu Mingkun ,and Cao Shuang . TRANSFORMATION OF GPS HEIGHT BASED ONGENERAL REGRESSION NEURAL NETWORK[J]. jgg, 2011, 31(6): 113-116.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2011/V31/I6/113
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