Machine learning application to predict the electron temperature on the J-TEXT tokamak

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.