利用人工神经网络研究电离层参量变化

利用人工神经网络研究低纬电离层参量的预测，首先我们研究从某一个月的电离层月中值预测下一个月的月中值。

A Study of Ionospheric Parameters Using the Artificial Neural Net

In the present paper, variations of ionorpheric parameters at lower latitudes are studied by using theArtificial Neutal Net (ANN). First, predicting the monthly median values of f0F2 from the monthly median of thelast month mean values is conducted. Analysis of calculated errors indicates that tile variation characteristic of ionospheric parameter changes with time during different parts of day and night. They may be controlled by different factors and obey different variation rules. In order to get a high prediction precision I we have predicted the monthly median values of f0F2 in two or three seperated time ranges. If we use a single neural net to predict ionospheric parameters for a day, mean errors are less than 10% I but the errors may decrease to 5-8% if the time ranges are subdivided into two or three. Next, we use data of the solar radiation flux as the input of the ANN, and seek the nonlinearrelation between ionospheric parameters and the solar radiation flux. The data of both stations, Hainan andGuangzhou, for 11 years are used to train the ANd. The predicted results are better than one of IRI-90, and moreclose to the observed ones. The first results show that the ADN can use a large amount of ionospheric data, and obtain complicated nonlinear relation in the ionosphere. At the same time, it is also a very useful, and an artificial interligence technology for predicting ionocpheric parameters.