舰船电子工程2019，Vol.39Issue(4)：29-33,93,6.DOI:10.3969/j.issn.1672-9730.2019.04.007
一种基于RBF神经网络的大气温度及水汽密度廓线反演方法
An Inversion Method of Atmospheric Temperature and Water Vapor Density Profile Based on RBF Neural Network
吕新帅 1田斌 1梁翔 1谭玉霖 1刘圣良2
作者信息
- 1. 中国人民解放军海军工程大学 武汉 430033
- 2. 中国人民解放军91668部队 上海 200083
- 折叠
摘要
Abstract
By using the ground-based 16-channel microwave radiometer to analyse the data in Wuhan(including 16 channels of brightness temperature data and surface meteorological information data)and corresponding sounding data(including tempera?ture profile and water vapour density profile)to form the network training samples and testing samples,the observation data in the training samples and the corresponding sounding data are respectively used as input and output data for training the neural net?works,and the observation data in the test samples are used as input data to test the trained neural networks. The sounding data of the test sample is compared as standard to the test output. By comparing the training results and the test results of RBF neural net?work and BP neural network,the prediction accuracy and feasibility of RBF neural network under uncertain conditions are verified. The experimental results show that the RBF neural network has faster calculation speed,more accurate inversion capability and stronger generalization ability than the BP neural network in retrieving the atmospheric temperature profile and the water vapour den?sity profile. The advantages of using RBF neural network to invert atmospheric temperature profile and water vapour density profile are significantly greater than BP neural network inversion method. Applying this method to microwave radiometer is of great signifi?cance for improving the level of inversion technology.关键词
RBF神经网络/微波辐射计/大气廓线反演Key words
RBF neural network/microwave radiometer/atmospheric profile inversion分类
信息技术与安全科学引用本文复制引用
吕新帅,田斌,梁翔,谭玉霖,刘圣良..一种基于RBF神经网络的大气温度及水汽密度廓线反演方法[J].舰船电子工程,2019,39(4):29-33,93,6.
