Document Detail


Support vector machine-based feature extractor for L/H transitions in JET.
MedLine Citation:
PMID:  21061485     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
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.
Authors:
S González; J Vega; A Murari; A Pereira; J M Ramírez; S Dormido-Canto;
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Review of scientific instruments     Volume:  81     ISSN:  1089-7623     ISO Abbreviation:  Rev Sci Instrum     Publication Date:  2010 Oct 
Date Detail:
Created Date:  2010-11-08     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0405571     Medline TA:  Rev Sci Instrum     Country:  United States    
Other Details:
Languages:  eng     Pagination:  10E123     Citation Subset:  -    
Affiliation:
Asociación EURATOM/CIEMAT para Fusión, Madrid 28040, Spain. sergio.gonzalez@ciemat.es
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