Geometrical kernel machine for prediction and novelty detection of disruptive events in TOKAMAK machines

Barbara Cannas, Rita Sabrina Delogu, Alessandra Fanni, Augusto Montisci, Piergiorgio Sonato, Katiuscia Zedda

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Abstract

This paper presents a recently introduced Kernel Machine, called Geometrical Kernel Machine, used to predict disruptive events in nuclear fusion reactors. The algorithm proposed to construct the Kernel Machine is able to automatically determine both the number of neurons and the synaptic weights of a Multilayer Perceptron neural network with a single hidden layer. It has been demonstrated that the resulting network is able to classify any finite set of patterns defined in a real domain. The prediction problem has been here modeled as a two classes classification problem. The geometrical interpretation of the network equations allows us both to develop the disruption predictor and to manage the so called ageing of the kernel machine. In fact, using the same kernel machine, a novelty detection system has been integrated in the predictor, increasing the overall system performance, and the reliability of the predictor. © 2009 Springer Science+Business Media, LLC.
Original languageEnglish
Pages (from-to)85 - 93
Number of pages9
JournalJournal of Signal Processing Systems
Volume61
Issue number1
DOIs
Publication statusPublished - 1 Oct 2010
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Hardware and Architecture

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