Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Plasma disruption in a Tokamak reactor is a sudden loss of magnetic confinement that can cause damage to the machine walls and the support structures. For this reason early detection of the onset of such an event is of practical interest. This paper presents a novel technique for early prediction of plasma disruption in Tokamak reactors based on chaos theory and a comparison of neural networks; neuro-fuzzy inference systems are also presented. In particular, dynamical reconstruction and chaos theory have been considered for choosing the time window of prediction and to select the set of inputs for the prediction system. Multi-layer-perceptron nets and Sugeno's neuro-fuzzy inference have been exploited for predicting the onset of disruption. Within the limits of the available database (disruptive discharges in JET machine) it is possible to predict the onset of the disruptive event sufficiently in advance to activate the control system.