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A machine-learning-based epistemic modeling framework for textile antenna design

Tijdschriftbijdrage - Tijdschriftartikel

A novel machine-learning-based framework to evaluate the effect of design parameters affected by epistemic uncertainty on the performance of textile antennas is presented in this letter. In particular, epistemic variations are characterized in the framework of possibility theory, which is combined with Bayesian optimization to accurately and efficiently perform uncertainty quantification. A suitable application example validates the proposed method.
Tijdschrift: IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS
ISSN: 1548-5757
Issue: 11
Volume: 18
Pagina's: 2292 - 2296
Jaar van publicatie:2019
BOF-keylabel:ja
IOF-keylabel:ja
BOF-publication weight:0.1
Auteurs:International
Authors from:Higher Education
Toegankelijkheid:Closed