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Neural networks for broad-band evaluation of complex permittivity using a coaxial discontinuity

Published online by Cambridge University Press:  30 May 2007

H. Acikgoz ,
Y. Le Bihan ,
O. Meyer  and
L. Pichon
H. Acikgoz*
Affiliation:
Laboratoire de Génie Électrique de Paris – LGEP, CNRS UMR 8507, Supelec, Univ. Pierre et Marie Curie-P6, Univ. Paris Sud-P11, Plateau de Moulon, 11 rue Joliot Curie, 91192 Gif-Sur-Yvette Cedex, France
Y. Le Bihan
Affiliation:
Laboratoire de Génie Électrique de Paris – LGEP, CNRS UMR 8507, Supelec, Univ. Pierre et Marie Curie-P6, Univ. Paris Sud-P11, Plateau de Moulon, 11 rue Joliot Curie, 91192 Gif-Sur-Yvette Cedex, France
O. Meyer
Affiliation:
Laboratoire de Génie Électrique de Paris – LGEP, CNRS UMR 8507, Supelec, Univ. Pierre et Marie Curie-P6, Univ. Paris Sud-P11, Plateau de Moulon, 11 rue Joliot Curie, 91192 Gif-Sur-Yvette Cedex, France
L. Pichon
Affiliation:
Laboratoire de Génie Électrique de Paris – LGEP, CNRS UMR 8507, Supelec, Univ. Pierre et Marie Curie-P6, Univ. Paris Sud-P11, Plateau de Moulon, 11 rue Joliot Curie, 91192 Gif-Sur-Yvette Cedex, France
*
hulusi.acikgoz@lgep.supelec.fr
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Abstract

The aim of this study is to determine the complex permittivity of dielectric materials using a coaxial discontinuity and the combination of neural networks (NN) with the finite element method. Two types of measurement cells are used. One is for solid samples and the other one for liquids. Data sets used to train neural networks are created using the finite element method. The number of hidden neurons of the NN is determined by the split-sample method. The designed NN are used for the estimation of the permittivity of several materials and their results compared with the ones obtained with a gradient inversion method.

Keywords

Type
Research Article
Information
The European Physical Journal - Applied Physics , Volume 39 , Issue 2: NUMELEC 2006 , August 2007 , pp. 197 - 201
Copyright
© EDP Sciences, 2007

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References

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