An Exhaustive Employment of Neural Networks to Search the Better Configuration of Magnetic Signals in ITER Machine

Concerning the control of plasma column evolution in ITER machine, the reconstruction of the plasma shape in the vacuum vessel represents an important step. In this work, starting from magnetic measurements, a soft computing approach to estimate the distances of the plasma boundary from the first wall of the vacuum vessel is carried out by means of Neural Networks (NNs). In particular, Multi-Layer Perceptron (MLP) nets have been exploited for the purpose. Finally, to verify the robustness of the proposed approach, any different database and number of input parameters has been used.