Bayesian正则化BP神经网络拟合两类似大地水准面
Application of Bayesian Regulation BP Neural Network to Fit Two-Kind Quasi-geoid
-
摘要: 为限制重力似大地水准面拟合到GPS/水准似大地水准面上的模型代表性误差,提出了Bayesian正则化BP神经网络拟合两类似大地水准面的新方法。利用某区域的重力似大地水准面模型和GPS/水准数据,将新方法与传统的曲面拟合法进行比较。在较大区域和两类似大地水准面差别不规则的情况下,Bayes-ian正则化BP神经网络有效地减少了拟合模型的代表性误差,而且通过Bayesian正则化算法对网络权值进行限制,抑制了过拟合现象。新方法提高了两面拟合结果的内、外符合精度。Abstract: In order to restrict the models error of fitting gravimetric quasi-geoid to GPS/leveling quasi-geoid,the new method of fitting two kind quasi-geoid using Bayesian regulation BP neural network was proposed.Using the gravimetric quasi-geoid and GPS/leveling data in a certain area,the new method was compared with polynomial surface fitting method.In the case with biggish area and anomalous difference between two kind of quasi-geoid,Bayesian regulation BP neural network could reduce the erros of models,and Bayesian regulation arithmetic could improve the structure of network by restricting weights to produce a smoother network response.The experimental result shows that the new method can improve the inner and outer precisions of fitting two kinds of quasi-geoid clearly.