[1]高 宁,高彩云,吴良才.GPS高程迭加拟合模型的研究[J].西安科技大学学报,2009,(03):339-343.
 GAO Ning,GAO Cai-yun,WU Liang-cai.Research of combined model for GPS height[J].Journal of Xi'an University of Science and Technology,2009,(03):339-343.
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GPS高程迭加拟合模型的研究()
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2009年03期
页码:
339-343
栏目:
出版日期:
2009-06-11

文章信息/Info

Title:
Research of combined model for GPS height
文章编号:
1672-9315(2009)03-0339-05
作者:
高 宁1高彩云1吴良才2
1.平顶山工学院 测量与国土信息系,河南 平顶山 467001; 2.东华理工大学 地球科学与测绘工程学院,江西 抚州 344000
Author(s):
GAO Ning1GAO Cai-yun1WU Liang-cai2
1.Dept. of Survey & Land Information Engineering,Pingdingshan Institute of Technology,Pingdingshan 467001,China; 2.Geoscience and Surveying Engineering College, East China Institiute of Technology,Fuzhou 344000,China
关键词:
关键词: GPS高程 高程异常 遗传算法 神经网络 迭加模型
Keywords:
GPS height height anomaly genetic angthorim neural network combined model
分类号:
TD173+.2
文献标志码:
A
摘要:
摘 要: 为提高GPS高程拟合模型在逼近高程异常曲面时的逼近精度和可信度,探讨了GPS高程转换的迭加模型(遗传神经网络模型和神经网络综合模型)。通过实测的GPS水准数据对迭加模型和单一模型进行分析比较,结果表明迭加模型逼近高程的精度和可靠性均高于单一模型。
Abstract:
In order to improve approximating precision and reliability of GPS leveling of imitated model in approxi-mating anormal height curved surface, the paper discussed the combined model of GPS elevation height to normal height, such as the genetic neural network model and neural network combined model. We compared the two combined models with single models. The results indicate that the precision and reliability are better than any single model.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2008-09-01 基金项目: 国家测绘局重点实验室基金项目(KLM200818); 河南省平顶山工学院2008年院级基金资助项目 作者简介: 高 宁(1982-),男,河北保定人,工学硕士,讲师,主要从事“3S”理论及数据处理方面的研究工作.
更新日期/Last Update: 2009-06-11