Abstract Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibility of developing a neural network predictor that intervenes well in advance for avoiding plasma disruption or mitigating its effects.
Fund: Project supported by the National Natural Science Foundations of China
(Grant No~10775040) and partially by JSPS-CAS Core University Program on Plasma and
Nuclear Fusion.
Cite this article:
Wang Hao(王灏), Wang Ai-Ke(王爱科), Yang Qing-Wei(杨青巍), Ding Xuan-Tong(丁玄同), Dong Jia-Qi(董家齐), Sanuki H, and Itoh K HL-2A tokamak disruption forecasting based on an artificial neural network 2007 Chinese Physics 16 3738
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