Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

Murari, A., Lungaroni, M., Peluso, E., Gaudio, P., Vega, J., Dormido Canto, Sebastián, Baruzzo, M., Gelfusa, M. y JET Contributors . (2018) Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET. Nuclear Fusion

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Título Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET
Autor(es) Murari, A.
Lungaroni, M.
Peluso, E.
Gaudio, P.
Vega, J.
Dormido Canto, Sebastián
Baruzzo, M.
Gelfusa, M.
JET Contributors
Materia(s) Informática
Abstract Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy 'from scratch' has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.
Palabras clave disruptions
probabilistic SVM
machine learning predictors
decision support systems
Editor(es) IOP Publishing
Fecha 2018-03-02
Formato application/pdf
Identificador bibliuned:557-Sdormido-0064
DOI - identifier 10.1088/1741-4326/aaaf9c
ISSN - identifier 1741-4326
Nombre de la revista Nuclear Fusion
Número de Volumen 58
Publicado en la Revista Nuclear Fusion
Idioma eng
Versión de la publicación acceptedVersion
Tipo de recurso Article
Derechos de acceso y licencia
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in Nuclear Fusion, is available online at the publisher's website: IOP Publishing
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Nuclear Fusion, está disponible en línea en el sitio web del editor: IOP Publishing

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