Artificial Neural Networks for Classifying Magnetic Measurements in Tokamak Reactors

This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.

• A. Greco
• N. Mammone
• F.C. Morabito
• M.Versaci

• Tokamak
• sensors
• artificial neural network.

[1] J. Wesson, "Tokamaks", Oxford Science Pub., 1987.
[2] M. Matsukawa, et al., "Application of Regression Analysis to Deriving
Measurements Formulas for Feedback Control of Plasma Shape in JT-
60", Plasma Physics and Controlled Fusion, vol. 34, N┬░ 6, pp. 907-921,
1992.
[3] F.C. Morabito et al. "Final report on EFDA Study Contract FU05 CT
2002-00162 (EFDA 02-1001)", 2002.
[4] C. M. Bishop, "Neural Network for Pattern recognition", Clarendon
Press, Oxford, 1995.
[5] F.C. Morabito, et al., "On Line Plasma Shape Identification in a
Tokamak Reactor via Neural Network", Proc. Of V Italian Workshop on
Neural Networks, Word Scientific Publishing, p. 349, 1992.