A GNSS Elevation Fitting Method Based on Intelligent Algorithm
Generalized regression neural network(GRNN)is a new feed-forward neural network which has many advantages such as fewer training times,short period,strong forecasting capability of nonlinear parameters and etc.However,as the only adjustable parameter of GRNN,SPREAD cannot be obtained automatically,which limits its further application.To solve this drawback,Fly Optimization Algorithm(FOA)is combined with GRNN to build FOAGRNN model,which optimizes GRNN model and achieves automatic gathering of adjustable parameter.In order to test the accuracy of GNSS elevation fitting based on FOAGRNN model,an experimental analysis is carried out.The results show that the above accuracy of GNSS height fitting reaches 6 mm.FOAGRNN model is also compared with plane fitting model and quadric fitting model,which shows that the superiority of FOAGRNN model in fitting accuracy.In conclusion,FOAGRNN model supports higher accuracy of GNSS height fitting even though less data samples are available.
国家科技期刊平台
NSTL
万方数据
