摘要
将香港地区某天由电离层层析反演得到的电子密度值分成6组,利用神经网络方法对该6组数据分别进行了拟合建模及预报。实验结果表明,采用电离层层析技术并经神经网络模型预报得出的电子密度值精度明显高于由IRI2007模型提供的电子密度值,其预报的30min及60min的电子密度值精度可分别达到0.45TECU和1.34TECU。
The electron density values of one day in Hong Kong obtained by ionospheric to- mographic are divided into 6 groups. Then, the 6 groups data are fitted modeling and fore- casting by neural network method. The results show that the accuracy of electron density value forecasted by the tomographic and neural network model is significantly higher than the electron density value provided by IRI2007 model. The precision of the electron density fore- cast value within 30 minutes and 60 minutes can respectively arrive at 0.45 TECU and 1.34 TECU.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2012年第8期972-975,1013,共4页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(41004011)
中南大学前沿研究计划资助项目(2009QZZD002)
关键词
电离层层析
神经网络
电子密度值
GPS
ionospheric tomographic
neural network
electron density value
GPS
