Automatic disruption classification at JET: Comparison of different pattern recognition techniques

B. Cannas, F. Cau, A. Fanni, P. Sonato, M.K. Zedda

Research output: Contribution to journalArticle

17 Citations (Scopus)


In this paper, different pattern recognition techniques have been tested in order to implement an automatic tool for disruption classification in a tokamak experiment. The methods considered refer to clustering and classification techniques. In particular, the investigated clustering techniques are self-organizing maps and K-means, while the classification techniques are multi-layer perceptrons, support vector machines, and k- nearest neighbours. Training and testing data have been collected selecting suitable diagnostic signals recorded over 4 years of EFDA-JET experiments. Multi-layer perceptron classifiers exhibited the best performance in classifying mode lock, density limit/high radiated power, H-mode/L-mode transition and internal transport barrier plasma disruptions. This classification performance can be increased using multiple classifiers. In particular the outputs of five multi-layer perceptron classifiers have been combined using multiple classifier techniques in order to obtain a more robust and reliable classification tool, that is presently implemented at JET. © 2006 IAEA.
Original languageEnglish
Article number002
Pages (from-to)699 - 708
Number of pages10
JournalNuclear Fusion
Issue number7
Publication statusPublished - 1 Jul 2006
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Condensed Matter Physics

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