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

作　　者：丁永华 金雪松 陈真真 庄革 

机构地区：[1]State Key Laboratory of Advanced Electromagnetic Engineering and Technology, College of Electrical and Electronic Engineering, Huazhong University of Science and Technology

出　　处：《Plasma Science and Technology》2013年第11期1154-1159,共6页等离子体科学和技术（英文版）

基　　金：supported by the National Magnetic Confinement Fusion Science Program of China(Nos.2010GB107004,2011GB109001,2008CB717805);National Natural Science Foundation of China(Nos.11275080,10935004)

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.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.

关 键 词：DISRUPTION locked mode BP neural network prediction 

分 类 号：TL631.24[核科学技术—核技术及应用]