Microwave Characterization of Dielectric Materials Using Bayesian Neural Networks

Acikgoz, Hulusi; Bihan, Yann Le; Meyer, Olivier; Pichon, Lionel

doi:10.2528/pierc08030603

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Recently, many works on dielectric characterization using coaxial discontinuity have been realized with the new idea of artificial intelligence to solve the inverse problem. Acikgoz et al (2007Acikgoz et al ( , 2008 have introduced various artificial neural networks (ANN) as inversion methods for broad-band evaluation of complex permittivity of Poly EtherEtherKetone (PEEK), ethanol and water. Hacib et al ( , 2011 show the use of various support vector machines (SVM) as inversion tools for ethanol characterization.…”

Section: Introductionmentioning

confidence: 99%

An approach based on ANFIS and input selection procedure for microwave characterization of dielectric materials

Sadou

Hacib

Acikgoz

et al. 2018

COMPEL

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PurposeThe principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the measurements of the probe admittance (Y(f) = G(f)+ jB(f )). The purpose of this paper is to develop an alternative inversion tool able to predict the evolution of the complex permittivity (ε = ε′ – jε″) on a broad band frequency (f from 1 MHz to 1.8 GHz). Design/methodology/approachThe inverse problem is solved using adaptive network based fuzzy inference system (ANFIS) which needs the creation of a database for its learning. Unfortunately, train ANFIS using f, G and B as inputs has given unsatisfying results. Therefore, an inputs selection procedure is used to select the three optimal inputs from new inputs, created mathematically from original ones, using the Jang method. FindingsInversion results of measurements give, after training, in real time the complex permittivity of solid and liquid samples with a very good accuracy which prove the applicability of ANFIS to solve inverse problems in microwave characterization. Originality/valueThe originality of this paper consists on the use of ANFIS with input selection procedure based on the Jang method to solve the inverse problem where the three optimal inputs are selected from 26 new inputs created mathematically from original ones (f, G and B).

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Section: Introductionmentioning

confidence: 99%

An approach based on ANFIS and input selection procedure for microwave characterization of dielectric materials

Sadou

Hacib

Acikgoz

et al. 2018

COMPEL

Self Cite

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“…For further progress in capabilities, numerical method accuracy and hardware productivity, Eugene et al [16] start using finite difference time domain (FDTD) software tool in order to reduce the coast achievement of simple rectangular waveguide structure. More recently, Acikgoz et al [17] have investigated the use of Bayesian ANN algorithm. The latter leads to more improvement of dielectric characterization of materials.…”

Section: Introductionmentioning

confidence: 99%

Dielectric characterization using FEM modeling and ANNs for coaxial waveguide with conical open ended radiation

Afendi¹,

Mohamed²,

Tao³

et al. 2016

Int. J. Microw. Wireless Technol.

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Since last decades, microwaves have received tremendous attention as an interesting tool for material characterization. In general, standard microwave measurement methods require cutting and polishing of samples to put it in a suitable waveguide or cavity. However, several methods have been developed in order to permit a non-destructive measurement. A well-known method is based on coaxial open-ended waveguide, which is used as a sensor for dielectric characterizations. Moreover, with the requirement of new forms, developing mathematical model for each one is not convenient. Indeed, the complex structures required in the industrial field can be perfectly designed with high-performance three-dimensional software. Many attempts have been done to solve the conversion problem by proposing different algorithms. Nevertheless, they are sensitive for complex structure that contains transition part. In this paper, we propose a dielectric measurement method based on the use of coaxial waveguide. A novel algorithm for dielectric characterization of complex structures is also presented, which is based on the joint use of artificial neuronal networks and finite element method. The proposed algorithm aims to find the dielectric characterization for complex structures. Experimental evaluations applied to solid and liquid dielectrics confirm the validation of the proposed algorithm.

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“…Analytically directly extract the intrinsic elements is based on multi-bias S parameters measured by vector network analyzer. Although the values of intrinsic elements can be determined directly at different bias condition by a series of analytical closed function [5,16], the extracted values of these intrinsic elements remain some variations with frequency [16]. To store all the values at each frequency points will pay the price of using much storage memory and making the models run slowly.…”

Section: Ann-based Small-signal Modeling Techniquementioning

confidence: 99%

“…It is widely used in the optimization of passive components and microwave nonlinear device modeling [13][14][15][16][17][18]. The multiplayer perceptron (MLP) is a popularly applied neural network structure [8].…”

Section: Ann Modeling Technique Introductionmentioning

confidence: 99%

An Ann-Based Small-Signal Equivalent Circuit Model for Mosfet Device

Li¹,

Li²,

Quan³

2012

PIER

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Microwave Characterization of Dielectric Materials Using Bayesian Neural Networks

Cited by 9 publications

References 13 publications

An approach based on ANFIS and input selection procedure for microwave characterization of dielectric materials

An approach based on ANFIS and input selection procedure for microwave characterization of dielectric materials

Dielectric characterization using FEM modeling and ANNs for coaxial waveguide with conical open ended radiation

An Ann-Based Small-Signal Equivalent Circuit Model for Mosfet Device

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