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Subsurface Object Identification by Artificial Neural Networks and Impulse Radiolocation

机译:人工神经网络和脉冲放射性分配的地下对象识别

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The problem of identification of objects under ground surface is solved by the application of irradiation of the surface by short impulse electromagnetic waves and the use of artificial neural networks (ANN) for the analysis of reflected field characteristics. As input data for ANN the normalized amplitudes of electrical component of the field in determined points of observation in equidistant moments of time are used. As an example of the object for the identification, the metal tube under surface of a ground is considered. The plane electromagnetic wave having Gaussian time dependence is used as an incident field. The influence of a number of hidden layers of ANN on precision of the recognition is investigated.
机译:通过在短脉冲电磁波和使用人工神经网络(ANN)来分析反射场特征的情况下,通过应用表面照射来解决地面下物体识别的问题。作为ANN的输入数据,使用了在等距时段的确定观察点中的场的归一化的电气分量幅度。作为用于识别的对象的示例,考虑了地面的表面下的金属管。具有高斯时间依赖性的平面电磁波用作事件场。研究了一些H隐藏层关于识别精度的影响。

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