Geometrical Kernel Machine for prediction and novelty detection of disruptive events in TOKAMAK machines

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

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

This paper presents a so called Geometrical Kernel Machine used to predict disruptive events in nuclear fusion reactors. Here, the prediction problem is modeled as a two classes classification problem, and the predictor is built by using a new constructive algorithm that allows us to automatically determine both the number of neurons and the synaptic weights of a Multilayer Perceptron network with a single hidden layer. It has been demonstrated that the resulting network is able to classify any set of patterns defined in a real domain. The geometrical interpretation of the network equations allows us both to develop the 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. ©2007 IEEE.
Original languageEnglish
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - , Greece
Duration: 1 Jan 2007 → …

Conference

Conference17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
CountryGreece
Period1/1/07 → …

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Signal Processing

Cite this

Cannas, B., Delogu, R., Fanni, A., Montisci, A., Sonato, P., & Zedda, M. K. (2007). Geometrical Kernel Machine for prediction and novelty detection of disruptive events in TOKAMAK machines. Paper presented at 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007, Greece. https://doi.org/10.1109/MLSP.2007.4414342