Forecasting disruptions in the ADITYA tokamak using neural networks

A neural network technique has been used to predict disruptions in the ADITYA tokamak. A time series prediction method is employed whereby a series of past values of some time dependent quantity is used to predict its value in the future. The time varying observables used in the present work are the different diagnostic signals from four Mirnov probes, one soft X ray monitor and one Hα monitor. The predicted quantities are the same observables at some future time. The neural network is trained with the past values of the different diagnostic signals as inputs and the future values of the same quantities as targets. The trained neural network is used to forecast in a multistep sequence. This amounts to a prediction several time steps earlier. Very good prediction results have been obtained up to 8 ms earlier with little distortion of the signals and no appreciable time lag, a capability which is believed to be well suited to the task of on-line predictions of disruptions in ADITYA. As actual experimental signals are used, confidence regarding the performance of the neural network on hardware implementation is automatically ensured.