摘要
The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor.When some diagnostics fail to detect information about the plasma status,such as electron temperature,they can also be obtained by another method:fitted by other diagnostic signals through machine learning.The paper herein is based on a machine learning method to predict electron temperature,in case the diagnostic systems fail to detect plasma temperature.The fully-connected neural network,utilizing back propagation with two hidden layers,is utilized to estimate plasma electron temperature approximately on the J-TEXT.The input parameters consist of soft x-ray emission intensity,electron density,plasma current,loop voltage,and toroidal magnetic field,while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal spectrometer.Therefore,the temperature profile is reconstructed by other diagnostic signals,and the average errors are within 5%.In addition,generalized regression neural network can also achieve this function to estimate the temperature profile with similar accuracy.Predicting electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.
作者
董蛟龙
李建超
丁永华
张晓卿
王能超
李达
严伟
沈呈硕
何莹
任颉颃
夏冬辉
the J-TEXT Team
Jiaolong DONG;Jianchao LI;Yonghua DING;Xiaoqing ZHANG;Nengchao WANG;Da LI;Wei YAN;Chengshuo SHEN;Ying HE;Xiehang REN;Donghui XIA(International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics,State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,People's Republic of China;Hubei Key Laboratory of Optical Information and Pattern Recognition,Wuhan Institute of Technology,Wuhan 430205,People's Republic of China)
基金
supported by the National Magnetic Confinement Fusion Science Program(Nos.2018YFE0301104,2018YFE0301100)
State Key Laboratory of Advanced Electromagnetic Engineering and Technology(No.AEET2020KF001)
National Natural Science Foundation of China(Nos.12075096,51821005)。