Forecast of TEXT plasma disruptions using soft X rays as input signal in aneural network

A feedforward neural network with tate hidden layers is used to forecast ma jor and minor disruptive instabilities in TEXT tokamak discharges. Using th e experimental data of soft X ray signals as input data, the neural network is trained with one disruptive plasma discharge, and a different disruptiv e discharge is used for validation. After being properly trained, the netwo rks, with the same set of weights, are used to forecast disruptions in two other plasma discharges. It is observed that the neural network is able to predict the occurrence of a disruption more than 3 ms in advance. This time interval is almost 3 times longer than the one already obtained previously when a magnetic signal from a Mirnov coil was used to feed the neural netw orks. Visually no indication of an upcoming disruption is seen from the exp erimental data this far back from the time of disruption. Finally, by obser ving the predictive behaviour of the network for the disruptive discharges analysed and comparing the soft X ray data with the corresponding magnetic experimental signal, it is conjectured about where inside the plasma column the disruption first started.