Neural network prediction of some classes of tokamak disruptions

The use of neural network algorithms for predicting minor and major disruptions in tokamaks is explored by analysing disruption data from the TEXT tokamak with two network architectures. Future values of the fluctuating magnetic signal are predicted based oh L past values of the magnetic fluctuation signal measured by a single Mirnov coil. The time step used (= 0.04 ms) corresponds to the experimental data sampling rate. Two kinds of approach are adopted for the network: the contiguous future prediction and the multi-time-scale prediction. Both networks are trained through the back-propagation algorithm with inertial terms and the strengths of the results are compared. The use of additional diamagnetic signals as a method of increasing the performance is suggested. The degree of success indicates that the magnetic fluctuations associated with the TEXT disruption data may be characterized by a low dimensional dynamical system.