Postprocessing of Near-Field Measurement Based on Neural Networks
Postprocessing of Near-Field Measurement Based on Neural Networks
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DOI：
10.1109/tim.2010.2050373
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发表时间：
2011-02
影响因子：
5.6
通讯作者：
Ryadh Brahimi;Adam Kornaga;M. Bensetti;D. Baudry;Z. Riah;A. Louis;B. Mazari
中科院分区：
文献类型：
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作者：
Ryadh Brahimi;Adam Kornaga;M. Bensetti;D. Baudry;Z. Riah;A. Louis;B. Mazari
本文提出了基于神经网络（NN）模型的后处理方法，以提高一个或不同频率的空间分辨率重建磁场近场剖面。该模型旨在减少进行近场电磁兼容性（EMC）测量所需的时间。多层感知器神经网络（MLP）用于确定无源器件和电力电子元件的近磁场辐射。一种被称为分裂样本的优化方法被实现来确定神经网络的结构。用该方法得到的结果与实测结果进行了比较。创建了图形界面（GUI）来简化所开发的神经网络模型的使用。
This paper presents postprocessing based on neural network (NN) models to reconstruct the magnetic near-field profile with an improved spatial resolution for one or different frequencies. The models aim at decreasing the time required to perform near-field electromagnetic compatibility (EMC) measurements. The multilayer perceptron (MLP) NNs are used to determine the magnetic near field radiated by passive devices and power electronics components. An optimization method, called the split-sample method, is implemented to determine the structures of the NN. The results obtained with the proposed method are compared with the measurement results. A graphic interface (GUI) is created to simplify the utilization of the developed NN models.