Abstract Information on the state of the ionosphere is of great importance for organizing communications in the short-wave range. One of the main methods for estimating the ionosphere parameters is the use of modeling. Most existing ionosphere models rely on the algorithms presented in the recommendations of the Radiocommunication Sector of the International Telecommunications Union. This work shows one of the algorithm modifications for predicting the critical frequency of the ionospheric layer F2 using a modern approach based on artificial neural networks and data obtained from existing ionosondes. Accuracy estimation of determining the critical frequency of the F2 layer showed the advantage of the proposed technique and the possibility of its distribution to the estimation of the remaining parameters of the ionosphere during its modeling.

中文翻译：

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使用人工神经网络提高电离层模型精度

摘要 电离层状态信息对于组织短波范围内的通信非常重要。估计电离层参数的主要方法之一是使用建模。大多数现有的电离层模型依赖于国际电信联盟无线电通信部门的建议中提出的算法。这项工作展示了使用基于人工神经网络和从现有电离探空仪获得的数据的现代方法来预测电离层 F2 临界频率的算法修改之一。确定 F2 层临界频率的精度估计表明了所提出的技术的优势及其在建模过程中分布到电离层剩余参数估计的可能性。