This paper uses time delay neural network (TDNN) for predicting electromagnetic (EM) fields scattered from dielectric objects (cylinder, cylinder‐hemisphere, and cylinder‐cone) using: (a) FDTD generated initial field data for similar conducting objects and (b) Statistical information for the nature of fields. Statistical data indicated that the scattered field nature is close to deterministic. The TDNN structure determination uses statistical data for fixing the number of delays and tabular technique to obtain the number of hidden neurons. The TDNN training uses the Levenberg‐Marquardt (LM) algorithm. The model outputs follow standard FDTD results closely.

中文翻译：

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有限差分时域训练时延神经网络的电磁散射场时间序列

本文使用时延神经网络（TDNN）预测从介电物体（圆柱，圆柱半球和圆柱圆锥）散射的电磁（EM）场，方法是：（a）FDTD为相似的导电物体生成初始场数据，并且（b ）有关字段性质的统计信息。统计数据表明，散射场的性质接近确定性。TDNN结构确定使用统计数据确定延迟数，并使用表格技术获得隐藏神经元的数量。TDNN训练使用Levenberg-Marquardt（LM）算法。模型输出紧密遵循标准FDTD结果。