Fast diagnosis of transmission lines using neural networks and principal component analysis

被引：5
|
作者：
Smail, M. K. [1 ]
Le Bihan, Y. [1 ]
Pichon, L. [1 ]
机构：
[1] Univ Paris 06, SUPELEC, CNRS, UMR 8507,Lab Genie Elect Paris, F-91192 Gif Sur Yvette, France
来源：
INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS | 2012年 / 39卷 / 1-4期
关键词：
Wiring network; multiconductor transmission lines; FDTD method; time domain reflectometry; reconstruction; neural network and principal component analysis;
D O I：
10.3233/JAE-2012-1493
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A fast diagnosis dedicated to embedded transmission lines and based on time domain reflectometry is presented. The forward problem allows to simulate the propagation along the wiring network as well as to create datasets for the inverse problem resolution. Neural networks (NNs) are used to solve the inverse problem which consists to find defects on the wire from the reflectometry response. The significant parameters of the reflectometry response data are extracted using principal component analysis. This method allows an efficient reduction of the dimension of the reflectometry data space.
引用
收藏
页码：435 / 441
页数：7
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