Support vector machine-based feature extractor for L/H transitions in JET
- Asociacion EURATOM/CIEMAT para Fusion, Madrid 28040 (Spain)
- Consorzio RFX, Associazione EURATOM ENEA per la Fusione, Padova 4-35127 (Italy)
- Departamento de Informatica y Automatica, UNED, Madrid 28040 (Spain)
Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.
- OSTI ID:
- 22055851
- Journal Information:
- Review of Scientific Instruments, Vol. 81, Issue 10; Other Information: (c) 2010 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6748
- Country of Publication:
- United States
- Language:
- English
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