Summary
2019 Joint International Symposium on Electromagnetic Compatibility and Asia-Pacific International Symposium on Electromagnetic Compatibility, Sapporo
2019
Session Number:FriAM1A
Session:
Number:FriAM1A.2
Source Reconstruction Method Based on Machine Learning Algorithms (I)
Heming Yao, Lijun Jiang, Wei Sha,
pp.-
Publication Date:2016/10/5
Online ISSN:2188-5079
DOI:10.34385/proc.58.FriAM1A.2
Summary:
This paper proposes a new source reconstruction method (SRM) based on deep learning. The conventional SRM usually requires oversampled measurements data to ensure higher accuracy. Thus, conventional SRM numerical system is usually highly singular. A deep convolutional neural network (ConvNet) is proposed to reconstruct the equivalent sources of the target to overcome difficulty. The deep ConvNet allows us to employ less data samples. Besides, the ill-conditioned numerical system can be effectively avoided. Numerical examples are presented to demonstrate the feasibility and accuracy of the proposed method. Its performance is also compared with the traditional neural network and interpolation method. Moreover, we further expand the proposed method to measure the permittivity of dielectric scatterers.