Prediction of ionospheric electron concentration based on Wavelet Neural Network

N. Weijun, Zhang Xueqing, Gao Hefei
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Abstract

Short-wave communication plays an irreplaceable role in many fields, such as long-distance communiton. However, the realization of long-distance short-wave communicaiton depends on the state of the ionosphere. So how to predict the state of ionosphere, especially the electron concentration, has become the focus of current research direction to the ionosphere. In this paper, IRI data and wavelet neural network are used to model the prediction of ionospheric electron concentration, and the effective prediction of ionospheric electron concentration is realized. The accuracy and prediction period are improved. The applicability of season and latitude to the model is also discussed. The paper may help us know more about the ionosphere.
基于小波神经网络的电离层电子浓度预测
短波通信在许多领域发挥着不可替代的作用,如长途通信。然而,远距离短波通信的实现依赖于电离层的状态。因此如何预测电离层的状态,特别是电子浓度,已成为当前电离层研究的重点方向。本文利用IRI数据和小波神经网络对电离层电子浓度进行建模预测,实现了电离层电子浓度的有效预测。提高了预测精度和预测周期。讨论了季节和纬度对模型的适用性。这篇论文可以帮助我们更多地了解电离层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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