RESEARCH ON REFINEMENT OF REGIONAL QUASI-GEOID OF POYANG LAKE
Peng Xiangguo; Wan Xianbin; and Wang Hailong
Jiangxi Provincial Water Conservancy Planning and Designing Institute, Nanchang 330029
Abstract This paper proposes the BP neural network method which is based on particle swarm optimization algorithm to refine the regional quasigeoid, it by selecting the neural network weights and thresholds reasonably can effectively avoid the defect that the network slow convergence and easily trapped into the local optimal. This method is verified through the data of Poyang Lake and the results show that PSOBP neural network model can still obtain a good effect in the compared with the two surface partition fitting method absence of considering terrain conditions, which is selected according to the height anomaly variation.
Key words ：
Poyang lake
GPS
quasigeoid
PSO(Particle Swarn Oplimization)
BP neural network
Received: 01 January 1900
Corresponding Authors:
Peng Xiangguo
Cite this article:
Peng Xiangguo,Wan Xianbin,and Wang Hailong. RESEARCH ON REFINEMENT OF REGIONAL QUASI-GEOID OF POYANG LAKE[J]. , 2013, 33(1): 150-152.
Peng Xiangguo,Wan Xianbin,and Wang Hailong. RESEARCH ON REFINEMENT OF REGIONAL QUASI-GEOID OF POYANG LAKE[J]. jgg, 2013, 33(1): 150-152.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2013/V33/I1/150
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