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Neuro-genetic system for optimization of GMI samples sensitivity.
Pitta Botelho, A C O; Vellasco, M M B R; Hall Barbosa, C R; Costa Silva, E.
Afiliación
  • Pitta Botelho AC; Pontifical Catholic University of Rio de Janeiro, Department of Electrical Engineering, Brazil.
  • Vellasco MM; Pontifical Catholic University of Rio de Janeiro, Department of Electrical Engineering, Brazil. Electronic address: marley@ele.puc-rio.br.
  • Hall Barbosa CR; Pontifical Catholic University of Rio de Janeiro, Postgraduate Program in Metrology, Brazil.
  • Costa Silva E; Pontifical Catholic University of Rio de Janeiro, Department of Electrical Engineering, Brazil.
Neural Netw ; 75: 141-9, 2016 Mar.
Article en En | MEDLINE | ID: mdl-26775132
Magnetic sensors are largely used in several engineering areas. Among them, magnetic sensors based on the Giant Magnetoimpedance (GMI) effect are a new family of magnetic sensing devices that have a huge potential for applications involving measurements of ultra-weak magnetic fields. The sensitivity of magnetometers is directly associated with the sensitivity of their sensing elements. The GMI effect is characterized by a large variation of the impedance (magnitude and phase) of a ferromagnetic sample, when subjected to a magnetic field. Recent studies have shown that phase-based GMI magnetometers have the potential to increase the sensitivity by about 100 times. The sensitivity of GMI samples depends on several parameters, such as sample length, external magnetic field, DC level and frequency of the excitation current. However, this dependency is yet to be sufficiently well-modeled in quantitative terms. So, the search for the set of parameters that optimizes the samples sensitivity is usually empirical and very time consuming. This paper deals with this problem by proposing a new neuro-genetic system aimed at maximizing the impedance phase sensitivity of GMI samples. A Multi-Layer Perceptron (MLP) Neural Network is used to model the impedance phase and a Genetic Algorithm uses the information provided by the neural network to determine which set of parameters maximizes the impedance phase sensitivity. The results obtained with a data set composed of four different GMI sample lengths demonstrate that the neuro-genetic system is able to correctly and automatically determine the set of conditioning parameters responsible for maximizing their phase sensitivities.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Fenómenos Magnéticos / Modelos Genéticos Tipo de estudio: Diagnostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2016 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Fenómenos Magnéticos / Modelos Genéticos Tipo de estudio: Diagnostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2016 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos