Abstract
The L-H transition is a remarkable self-organization phenomenon that occurs in Magnetically Confined Nuclear Fusion (MCNF) devices. For research reasons, it is relevant to create models able to determine the confinement regime the plasma is in by using, from the wide number of measured signals in each discharge, just a reduce number of them. Also desirable is that a general model, applicable not only to one device but to all of them, is reached. From a data-driven modelling point of view it implies the careful —and hopefully, automatic— selection of the phenomenon’s related signals to input them into an equation able to determine the confinement mode. Using a supervised machine learning method, it would also require the tuning of some internal parameters. This is an optimization problem, tackled in this study with Genetic Algorithms (GAs). The results prove that reliable and universal laws that describe the L-H transition with more than a ~98,60% classification accuracy can be attained using only 3 input signals.
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Rattá, G.A., Vega, J. (2015). Confinement Regime Identification Using Artificial Intelligence Methods. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_28
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DOI: https://doi.org/10.1007/978-3-319-17091-6_28
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