摘要
为了准确而快速的对GPS高程进行拟合,文中将蚁群算法与BP神经网络算法相结合,建立了一种针对GPS高程拟合的蚁群神经网络模型,并结合实际的测量控制网数据进行了验证,结果表明:通过该模型计算得到的内符精度与外符精度较BP神经网络而言均有所提高,并且拟合效率更高;研究为后续工程测量提供了参考。
In order to accurately and quickly make the GPS elevation fitting, this paper, combining the ant colony al-gorithm and BP neural network algorithm fitting, establishes an ant colony neural network model for GPS height fit-ting which is then verified by the actual measurement control network data. The results show that the accuracy of theinternal and external symbols calculated by the proposed model is higher than that of the BP neural network, and thefitting efficiency is higher too. The study provides a reference for the follow -up projects.
出处
《矿山测量》
2016年第6期10-13,共4页
Mine Surveying
关键词
蚁群算法
BP神经网络
高程拟合
参数寻优
Ant colony algorithm
BP neural network
height fitting
parameter optimization
