%0 Journal Article
%T On the prediction of foF2 using artificial neural networks<br>利用人工神经网络预测电离层foF2参数
%A KONG Qing-Yan
%A LIU Wen
%A FAN Jun-Mei
%A JIAO Pei-Nan
%A FENG Jing
%A WANG Jun-Jiang
%A <br>孔庆颜
%J 地球物理学报
%D 2009
%I 
%X A method to predict oF2 one hour ahead using neural networks is developed. Based on the time variation characteristics of oF2, six parameters are determined as inputs by time series analysis, which are the present observation of oF2, one-day ahead oF2 at the same time as the predicted one, the seven-day average oF2 before the predicted hour and the current hour, the first difference of oF2 and time of day. The output is the oF2 one hour ahead. During high solar activity period, the average relative error is less than 6 percent and RMS less than 0.6 MHz, while during solar minimum, the average relative error is less than 10 percent and RMS less than 0.5 MHz.
%K Neural networks
%K foF2
%K Short-term prediction
%K Ionosphere<br>神经网络
%K foF2
%K 短期预测
%K 电离层
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=1E44AE713D8A6DE0&jid=14DC41C59CBF6770055A7D610D53AE46&aid=09A090CCB528568AB17626386E10F660&yid=DE12191FBD62783C&vid=286FB2D22CF8D013&iid=B31275AF3241DB2D&sid=5A66347DEC1DE6F4&eid=C2A302D88B1505F1&journal_id=0001-5733&journal_name=地球物理学报&referenced_num=0&reference_num=0