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Improved measurement of complex permittivity using artificial neural networks with scaled inputs
Author(s) -
Hasan Azhar,
Peterson Andrew F.
Publication year - 2011
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.26221
Subject(s) - permittivity , microwave , artificial neural network , additive white gaussian noise , range (aeronautics) , dissipative system , noise (video) , reflection (computer programming) , reflection coefficient , materials science , computational physics , physics , optics , electronic engineering , computer science , acoustics , white noise , optoelectronics , engineering , telecommunications , artificial intelligence , dielectric , image (mathematics) , quantum mechanics , composite material , programming language
A procedure is described to enhance the accuracy of microwave measurements of the complex permittivity of a dissipative medium.Monopole probe measurements are used in conjunction with two real‐valued neural networks, which are integrated together to reconstruct the complex permittivity from the measured reflection coefficients. The approach is tested over the frequency range from 2.5 to 5 GHz, for the real part of the permittivity in the range 3–10 and the imaginary part in the range 0– 0.5. The performance of the network is also demonstrated for a reduced frequency range from 3.5 to 5 GHz. Less than 4% error was observed in the presence of white Gaussian noise with an SNR of 10dB. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26221