Support vector machines for disruption prediction and novelty detection at JET

B. Cannas, R.S. Delogu, A. Fanni, P. Sonato, M.K. Zedda

Research output: Contribution to journalArticle

15 Citations (Scopus)


In the last years there has been a growing interest on black box approaches to disruption prediction. The drawback of these approaches is that the system could deteriorate its performance once it does not get updated. This could be the case of a disruption predictor for JET, where new plasma configurations might present features completely different from those observed in the experiments used during the training phase. This 'novelty' can be incorrectly classified by the system. A novelty detection method, which determines the novelty of the input of the prediction system, can be used to assess the system reliability. This paper presents a support vector machines disruption predictor for JET, wherein multiple plasma diagnostic signals are combined to provide a composite impending disruption warning indicator. In a support vector machine the analysis of the decision function value gives useful information about the novelty of an input and, on the reliability of the predictor output, during on-line applications. Results show the suitability of support vector machines both for prediction and novelty detection tasks at JET. © 2007 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)1124 - 1130
Number of pages7
JournalFusion Engineering and Design
Issue number5-14
Publication statusPublished - Oct 2007
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Materials Science(all)
  • Nuclear Energy and Engineering
  • Mechanical Engineering

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