基于二次曲面和BP神经网络组合模型的GPS高程异常拟合

摘要

The combined model based on the quadratic surface model and BP neural network model is applied to the GPS height anomaly fitting, while the combination is determined from the variance reciprocal method and general regression neural network (GRNN). The GPS elevation data in a certain area is used, the results show that both the accuracy and reliability with the combined model are more superior to the single models, and the fitting accuracy with the combined model based on general regression neural network ( GRNN) is better than that with the combined model based on the variance reciprocal method.%将二次曲面模型和BP神经网络的组合模型应用于高程异常拟合中,其组合方式分别基于方差倒数法和广义回归神经网络.利用某地区实测的GPS高程数据进行比较分析,结果表明,组合模型逼近高程异常的精度和可靠性均优于单一模型,并且基于广义回归神经网络的组合模型的拟合精度高于基于方差倒数法的组合模型.