Neural networks for FDTD‐backed permittivity reconstruction
ISSN: 0332-1649
Article publication date: 1 March 2005
Abstract
Purpose
To outline different versions of a novel method for accurate and efficient determining the dielectric properties of arbitrarily shaped materials.
Design/methodology/approach
Complex permittivity is found using an artificial neural network procedure designed to control a 3D FDTD computation of S‐parameters and to process their measurements. Network architectures are based on multilayer perceptron and radial basis function nets. The one‐port solution deals with the simulated and measured frequency responses of the reflection coefficient while the two‐port approach exploits the real and imaginary parts of the reflection and transmission coefficients at the frequency of interest.
Findings
High accuracy of permittivity reconstruction is demonstrated by numerical and experimental testing for dielectric samples of different configuration.
Research limitations/implications
Dielectric constant and the loss factor of the studied material should be within the ranges of corresponding parameters associated with the database used for the network training. The computer model must be highly adequate to the employed experimental fixture.
Practical implications
The method is cavity‐independent and applicable to the sample/fixture of arbitrary configuration provided that the geometry is adequately represented in the model. The two‐port version is capable of handling frequency‐dependent media parameters. For materials which can take some predefined form computational cost of the method is very insignificant.
Originality/value
A full‐wave 3D FDTD modeling tool and the controlling neural network procedure involved in the proposed approach allow for much flexibility in practical implementation of the method.
Keywords
Citation
Yakovlev, V.V., Murphy, E.K. and Eugene Eves, E. (2005), "Neural networks for FDTD‐backed permittivity reconstruction", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 24 No. 1, pp. 291-304. https://doi.org/10.1108/03321640510571318
Publisher
:Emerald Group Publishing Limited
Copyright © 2005, Emerald Group Publishing Limited