Application of Neural Networks for the Measurement of Electronic Temperature in Nuclear Fusion Experiments

Langmuir probes are used in nuclear fusion experiments to derive the electronic temperature of a high temperature ionised gas. For this purpose, the current I flowing through the probe at varying applied potentials V is measured. The shape of the resulting V-I characteristic is exponential, and depends upon the electronic temperature. Traditionally, fitting procedures are used to derive the temperature, but the amount of computation required is high as the measurement is repeated many times during the experiment. In this paper the use of neural networks to derive the electronic temperature from Langmuir probe data is investigated. Neural networks proved to be comparable with traditional methods in the accuracy of the reconstruction, though requiring less computational resources.