Neural networks for broad-band evaluation of complex permittivity using a coaxial discontinuity
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
Corresponding author: email@example.com
Accepted: 3 April 2007
Published online: 30 May 2007
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.
PACS: 07.05.Mh – Neural networks, fuzzy logic, artificial intelligence / 84.37.+q – Measurements in electric variables (including voltage, current, resistance, capacitance, inductance, impedance, and admittance, etc.)
© EDP Sciences, 2007