| 线状工程GPS高程拟合方法研究 |
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| 引用本文： | 袁晓燕,石晨阳,柴香,曹海迪.线状工程GPS高程拟合方法研究[J].全球定位系统,2021,46(6):74-77. |
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| 作者姓名： | 袁晓燕 石晨阳 柴香 曹海迪 |
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| 作者单位： | 重庆交通大学, 重庆 400074 |
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| 基金项目： | 城市空间信息工程北京市重点实验室经费资助项目(2020211) |
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| 摘 要： | 针对数学模型只适用于特定地形和神经网络模型易陷入局部最优解的特点，提出了最优加权算法，分别利用两种数学模型和两种神经网络模型进行线状工程GPS高程拟合，并将每种模型拟合效果好的单一算法组合成最优加权算法. 结果显示:多项式拟合法优于多面函数法，遗传模拟退火算法优化BP神经网络算法(GSA-BP)优于其他算法，为最优单一算法；加权算法比其单一成员算法精度分别提高17.7%、10.0%，且能基本满足四等水准测量要求，在线状工程GPS高程拟合中具有可行性.
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| 关 键 词： | 高程异常 多项式拟合 BP神经网络 加权 四等水准 |
| 收稿时间： | 2021-05-31 |
Research on GPS elevation fitting method for linear engineering |
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| Institution: | Chongqing Jiaotong University, Chongqing 400074, China |
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| Abstract: | Aiming at the characteristics that mathematical models are only applicable to specific terrain and neural network models are easy to fall into local optimal solution, an optimal weighting algorithm is proposed. Two mathematical models and two neural network models are used to fit the GPS elevation of linear engineering respectively, and a single algorithm with good fitting effect of each model is combined into an optimal weighting algorithm. The results show that the polynomial fitting algorithm is superior to the multifaceted function method, and the genetic simulated annealing algorithm optimized BP neural network algorithm (GSA-BP) is better than other algorithms, which is the best single algorithm. The precision of the weighted algorithm is 17.7% and 10.0% higher than that of the single member algorithm, respectively, and can basically meet the requirements of the fourth grade leveling, which is feasible in GPS elevation fitting of linear engineering. |
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