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A classification of meteor radio echoes based on artificial neural network
Open Astronomy ( IF 0.7 ) Pub Date : 2018-12-01 , DOI: 10.1515/astro-2018-0037
Mikhail Danilov , Arkadi Karpov

Abstract An artificial neural network is described for classification of meteor trails into the distinct overdense, intermediate and underdense trail categories. The neural network was trained and on model data obtained using the “KAMET” program and tested on real data. The best result of classification success rate of 95% without according to the heights of the formation of meteor trails. Results of classification with according to the heights of the formation of meteor trails are 82% - 91%.

中文翻译:

基于人工神经网络的流星无线电回波分类

摘要 描述了一种人工神经网络,用于将流星轨迹分类为不同的密集、中等和低密度轨迹类别。神经网络在使用“KAMET”程序获得的模型数据上进行训练,并在真实数据上进行测试。最好的结果分类成功率为95%,无需根据流星轨迹形成的高度。根据流星轨迹形成高度的分类结果为82% - 91%。
更新日期:2018-12-01
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