Calibration of nonlinear variable loads based on manifold learning

Autores
Venere, Alejandro Javier; Hurtado, Martín; Muravchik, Carlos Horacio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this work, we present a method for calibrating non-linear variable impedances based on the manifold-learning technique. This approach circumvents the dependency on the analytical model of the device, and works under the assumption that the impedance values come from a ”black box” controlled by a number of independent parameters. The goal of the calibration is to recover the unknown control parameters that set the load into the desired impedance states. We tested the proposed procedure first on a simulated example and then on the prototype presented in [1] at a frequency of 1575.42 MHz. The results based on both simulated and real data showed accurate recovery of the control parameters.
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
Materia
Ingeniería
Diffusion map
Manifold learning
Variable loads
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/154145
Acceder
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network_name_str SEDICI (UNLP)
spelling Calibration of nonlinear variable loads based on manifold learningVenere, Alejandro JavierHurtado, MartínMuravchik, Carlos HoracioIngenieríaDiffusion mapManifold learningVariable loadsIn this work, we present a method for calibrating non-linear variable impedances based on the manifold-learning technique. This approach circumvents the dependency on the analytical model of the device, and works under the assumption that the impedance values come from a ”black box” controlled by a number of independent parameters. The goal of the calibration is to recover the unknown control parameters that set the load into the desired impedance states. We tested the proposed procedure first on a simulated example and then on the prototype presented in [1] at a frequency of 1575.42 MHz. The results based on both simulated and real data showed accurate recovery of the control parameters.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/154145enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-544-754-7info:eu-repo/semantics/altIdentifier/doi/10.23919/RPIC.2017.8214362info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-11-05T13:19:13Zoai:sedici.unlp.edu.ar:10915/154145Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-05 13:19:13.484SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Calibration of nonlinear variable loads based on manifold learning
title Calibration of nonlinear variable loads based on manifold learning
spellingShingle Calibration of nonlinear variable loads based on manifold learning
Venere, Alejandro Javier
Ingeniería
Diffusion map
Manifold learning
Variable loads
title_short Calibration of nonlinear variable loads based on manifold learning
title_full Calibration of nonlinear variable loads based on manifold learning
title_fullStr Calibration of nonlinear variable loads based on manifold learning
title_full_unstemmed Calibration of nonlinear variable loads based on manifold learning
title_sort Calibration of nonlinear variable loads based on manifold learning
dc.creator.none.fl_str_mv Venere, Alejandro Javier
Hurtado, Martín
Muravchik, Carlos Horacio
author Venere, Alejandro Javier
author_facet Venere, Alejandro Javier
Hurtado, Martín
Muravchik, Carlos Horacio
author_role author
author2 Hurtado, Martín
Muravchik, Carlos Horacio
author2_role author
author
dc.subject.none.fl_str_mv Ingeniería
Diffusion map
Manifold learning
Variable loads
topic Ingeniería
Diffusion map
Manifold learning
Variable loads
dc.description.none.fl_txt_mv In this work, we present a method for calibrating non-linear variable impedances based on the manifold-learning technique. This approach circumvents the dependency on the analytical model of the device, and works under the assumption that the impedance values come from a ”black box” controlled by a number of independent parameters. The goal of the calibration is to recover the unknown control parameters that set the load into the desired impedance states. We tested the proposed procedure first on a simulated example and then on the prototype presented in [1] at a frequency of 1575.42 MHz. The results based on both simulated and real data showed accurate recovery of the control parameters.
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
description In this work, we present a method for calibrating non-linear variable impedances based on the manifold-learning technique. This approach circumvents the dependency on the analytical model of the device, and works under the assumption that the impedance values come from a ”black box” controlled by a number of independent parameters. The goal of the calibration is to recover the unknown control parameters that set the load into the desired impedance states. We tested the proposed procedure first on a simulated example and then on the prototype presented in [1] at a frequency of 1575.42 MHz. The results based on both simulated and real data showed accurate recovery of the control parameters.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/154145
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/doi/10.23919/RPIC.2017.8214362
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
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instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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