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.
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Acknowledgment
The authors would like to thank Mike Johnson and David Howell for providing the manual classification of the disruptions, and Tim Hender, Richard Buttery and Simon Pinches for supporting the work, and for the useful discussions.
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This work, supported in part by the Euratom Communities under the contract of Association between EURATOM/ENEA, was carried out within the framework of the European Fusion Development Agreement. The views and opinions expressed herein do not necessarily reflect those of the European Commission.
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Cannas, B., Delogu, R.S., Fanni, A. et al. Geometrical Kernel Machine for Prediction and Novelty Detection of Disruptive Events in TOKAMAK Machines. J Sign Process Syst 61, 85–93 (2010). https://doi.org/10.1007/s11265-009-0345-4
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DOI: https://doi.org/10.1007/s11265-009-0345-4