Artificial neural network modeling for improved coaxial line-reflect-match calibrations
- 1 January 2000
- journal article
- research article
- Published by Hindawi Limited in International Journal of RF and Microwave Computer-Aided Engineering
- Vol. 11 (1) , 33-37
- https://doi.org/10.1002/1099-047x(200101)11:1<33::aid-mmce4>3.0.co;2-c
Abstract
We model a coaxial load using an artificial neural network (ANN) to improve a coaxial line-reflect-match (LRM) calibration of an automatic network analyzer. The ANN is trained with measurement data obtained from a thru-reflect-line (TRL) calibration. The accuracy of the LRM calibration using the ANN-modeled load compares favorably to a benchmark multiline TRL calibration, with an average worst-case scattering-parameter error bound of 0.024 over an 18-GHz bandwidth. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 33–37, 2001.Keywords
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