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Applications of artificial neural networks to RF and microwave measurements
Author(s) -
Jargon Jeffrey A.,
Gupta K. C.,
DeGroot Donald C.
Publication year - 2002
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.10014
Subject(s) - artificial neural network , microwave , reflection coefficient , benchmark (surveying) , coaxial , calibration , electronic engineering , computer science , acoustics , engineering , electrical engineering , telecommunications , artificial intelligence , physics , mathematics , statistics , geodesy , geography
This article describes how artificial neural networks (ANNs) can be used to benefit a number of RF and microwave measurement areas including vector network analysis (VNA). We apply ANNs to model a variety of on‐wafer and coaxial VNA calibrations, including open‐short‐load‐thru (OSLT) and line‐reflect‐match (LRM), and assess the accuracy of the calibrations using these ANN‐modeled standards. We find that the ANN models compare favorably to benchmark calibrations throughout the frequencies they were trained for. We summarize other current applications of ANNs, including the determination of permittivities of liquids from the reflection coefficient measurements of an open‐ended coaxial probe and the determination of moisture content of wheat from free‐space transmission coefficient measurements. We also discuss some potential applications of ANN models related to power measurements, material characterization, and the comparison of nonlinear vector network analyzers. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 3–24, 2002.