Microwave characterization of dielectric materials using Bayesian neural networks

被引：9

作者：

Acikgoz, H. ^{[1
]}

Bihan, Y.L. ^{[1
]}

Meyer, O. ^{[1
]}

Pichon, L. ^{[1
]}

机构：

[1] Laboratoire de Génie Electrique de Paris, CNRS UMR8507, SUPELEC UPMC Univ. Paris 06, Univ. Paris-Sud, 11 rue Joliot-Curie, Plateau de Moulon, Gif-sur-Yvette Cedex,91192, France

来源：

Progress In Electromagnetics Research C | 2008年 / 3卷

关键词：

Finite element method - Permittivity - Dielectric materials - Bayesian networks;

D O I：

10.2528/PIERC08030603

中图分类号：

学科分类号：

摘要：

This paper shows the efficiency of neural networks (NN), coupled with the finite element method (FEM), to evaluate the broadband properties of dielectric materials. A characterization protocol is built to characterize dielectric materials and NN are used in order to provide the estimated permittivity. The FEM is used to create the data set required to train the NN. A method based on Bayesian regularization ensures a good generalization capability of the NN. It is shown that NN can determine the permittivity of materials with a high accuracy and that the Bayesian regularization greatly simplifies their implementation. © 2008, Electromagnetics Academy. All rights reserved.

引用

页码：169 / 182

共 50 条

[31] Using Bayesian neural networks to classify forest scenes
Vehtari, A
Heikkonen, J
Lampinen, J
Juujarvi, J
INTELLIGENT ROBOTS AND COMPUTER VISION XVII: ALGORITHMS, TECHNIQUES, AND ACTIVE VISION, 1998, 3522 : 66 - 73
[32] Bayesian training of neural networks using genetic programming
Marwala, Tshilidzi
PATTERN RECOGNITION LETTERS, 2007, 28 (12) : 1452 - 1458
[33] Quantifying Uncertainty of Portfolios using Bayesian Neural Networks
Esener, Suleyman
Wegner, Enrico
Almeida, Rui Jorge
Basturk, Nalan
Rodrigues, Paulo
2024 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN 2024, 2024,
[34] TensorBNN: Bayesian inference for neural networks using TensorFlow
Kronheim, B. S.
Kuchera, M. P.
Prosper, H. B.
COMPUTER PHYSICS COMMUNICATIONS, 2022, 270
[35] Using Bayesian neural networks to classify segmented images
Vivarelli, F
Williams, CKI
FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS, 1997, (440): : 268 - 273
[36] Bayesian Geophysical Inversion Using Invertible Neural Networks
Zhang, Xin
Curtis, Andrew
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 2021, 126 (07)
[37] Diagnosis of Headaches Types Using Artificial Neural Networks and Bayesian Networks
Fenerich, Amanda
Steiner, Maria Teresinha
Nievola, Julio Cesar
Mendes, Karina
Tsutsumi, Diego Paolo
dos Santos, Bruno
IEEE LATIN AMERICA TRANSACTIONS, 2020, 18 (01) : 59 - 66
[38] Synthesis and Characterization of Ceramic Dielectric Resonator Materials for Microwave Communication Technology
Tyagi, Anand K.
Parul
INTERNATIONAL CONFERENCE ON ADVANCES IN MANUFACTURING AND MATERIALS ENGINEERING (ICAMME 2014), 2014, 5 : 1322 - 1331
[39] THz Spectroscopy of Ceramic and Polycrystalline Materials for Characterization of Microwave Dielectric Properties
Siegrist, K. M.
Plusquellic, D. F.
Thomas, M. E.
35TH INTERNATIONAL CONFERENCE ON INFRARED, MILLIMETER, AND TERAHERTZ WAVES (IRMMW-THZ 2010), 2010,
[40] Microwave characterization of dielectric materials from 8 to 110 GHz using a free-space setup
Hollinger, RD
Jose, KA
Tellakula, A
Varadan, VV
Varadan, VK
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, 2000, 26 (02) : 100 - 105

← 1 2 3 4 5 →