Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak

摘要

Prediction of disruptions caused by locked modes using the Back-Propagation(BP)neural network is completed on J-TEXT tokamak.The network,which is based on the BP neural network,uses Mirnov coils and locked mode coils signals as input data,and outputs a signal including information of prediction of locked mode.The rate of successful prediction of locked modes is more than 90%.For intrinsic locked mode disruptions,the network can give a prewarning signal about 1 ms ahead of the locking-time.For the disruption caused by resonant magnetic perturbation(RMPs)locked modes,the network can give a prewarning signal about 10 ms ahead of the locking-time.