Author:
Hacib T.,Acikgoz H.,Le Bihan Y.,Mekideche M.R.,Meyer O.,Pichon L.
Abstract
PurposeThe dielectric properties of materials (complex permittivity) can be deduced from the admittance measured at the discontinuity plane of a coaxial open‐ended probe. This implies the implementation of an inversion procedure. The purpose of this paper is to develop a new non‐iterative inversion methodology in the field of microwave characterization allowing reducing the computation cost comparatively to iterative procedures.Design/methodology/approachThe inversion methodology combines the support vector machine (SVM) technique and the finite element method (FEM). The SVM are used as inverse models. They show good approximation and generalization capabilities. FEM allows the generation of the data sets required by the SVM parameter adjustment. A data set is constituted of input (complex admittance and frequency) and output (complex permittivity) pairs.FindingsThe results show the applicability of SVM to solve microwave inverse problems instead of using traditional iterative inversion methods which can be very time‐consuming. The experimental results demonstrate the accuracy which can be provided by the SVM technique.Practical implicationsThe paper allows extending the capability of microwave characterization cells developed at Laboratoire de Génie Électrique de Paris.Originality/valueA new inversion method is developed and applied to microwave characterization. This new concept introduces SVM in the context of microwave characterization. SVM results and iterative inversion procedure results are compared in order to evaluate the effectiveness of the developed technique.
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computational Theory and Mathematics,Computer Science Applications
Reference11 articles.
1. Acikgoz, H., Le Bihan, Y., Meyer, O. and Pichon, L. (2007), “Neural networks for broad‐band evaluation of complex permittivity using a coaxial discontinuity”, The European Physical Journal Applied Physics, Vol. 39 No. 2, pp. 197‐201.
2. Acikgoz, H., Le Bihan, Y., Meyer, O. and Pichon, L. (2008), “Microwave characterization of dielectric materials using Bayesian neural networks”, Progress in Electromagnetics Research C, Vol. 3, pp. 169‐82.
3. Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford University Press, New York, NY.
4. Cherkassky, V. and Ma, Y. (2004), “Practical selection of SVM parameters and noise estimation for regression”, Neural Networks, Vol. 17 No. 1, pp. 113‐26.
5. Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, Macmillan, New York, NY.
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献