| 径向基神经网络GPS高程转换方法 |
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| 引用本文： | 杨金玲,曹先革,曹先密.径向基神经网络GPS高程转换方法[J].地理空间信息,2010,8(6):65-66,70. |
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| 作者姓名： | 杨金玲 曹先革 曹先密 |
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| 作者单位： | [1]黑龙江工程学院测绘工程系,黑龙江哈尔滨150050 [2]东北林业大学林学院,黑龙江哈尔滨150040 [3]武汉大学测绘学院,湖北武汉430079 |
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| 基金项目： | 地理空间信息工程国家测绘局重点实验室开放基金，黑龙江工程学院基金 |
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| 摘 要： | 针对GPS高程转换问题,给出了基于径向基神经网络转换GPS高程的模型。用实际观测数据对该模型进行了试验,结果表明,用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。径向基神经网络能够有效克服BP神经网络局部极小值的缺点,并且具有较高的收敛速度,在GPS高程转换方面具有广阔应用前景。
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| 关 键 词： | 径向基神经网络 GPS高程 BP神经网络 |
GPS Height Conversion Methods Based on RBF Neural Network |
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| YANG Jinling,CAO Xiange,CAO Xianmi.GPS Height Conversion Methods Based on RBF Neural Network[J].Geospatial Information,2010,8(6):65-66,70. |
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| Authors: | YANG Jinling CAO Xiange CAO Xianmi |
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| Institution: | 1.Heilongjiang Institute of Technology,Harbin 150050,China;2.North-East Forestry University,Harbin 150040,China;3.Wuhan University,Wuhan 430079,China) |
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| Abstract: | This paper introduced a Radial Basis Function（RBF） Neural Network mode,which was applied to convert GPS height to normal height.The model was tested with observed data.The results showed that RBF Neural Network conversion accuracy than Quadratic fitting and BP Neural Network.RBF Neural Network can effectively overcome the local minimum shortcomings of BP Neural Network and has a high convergence rate;it has broad application prospects in the GPS height conversion. |
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| Keywords: | radial basis function neural network GPS height Back propagation Neural Network |
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