Disruption prediction using a long short-term memory (LSTM) algorithm has been developed on EAST, due to its inherent advantages in time series data processing. In the present work, LSTM is used as the model and the AUC (area under receiver operation characteristic curve) is used as the evaluation index. When the model is trained on data from the plasma current flattop phase and tested on data from the same period multiple times, the highest AUC is 0.8646 and the training time is about 6900 s per epoch. For comparison, the last 1000 ms of the flattop phases are intercepted as short time sequences. When the model is trained on data from short time sequences and tested on data from the same period, the highest AUC is increased to 0.9379 and the training time is restricted to 36 s per epoch. When the best model trained on the short time sequences is applied to the flattop phase for testing, the AUC is up to 0.9189. The experiment results show that it is possible for LSTM to train t...

中文翻译：

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使用EAST上的长期短期存储网络进行密度极限破坏预测

由于其在时序数据处理中的固有优势，因此已经在EAST上开发了使用长短期记忆（LSTM）算法的中断预测。在本工作中，将LSTM用作模型，并将AUC（接收机工作特性曲线下的面积）用作评估指标。当对模型进行等离子电流平顶阶段的数据训练，并多次对来自同一时期的数据进行测试时，最高AUC为0.8646，训练时间约为每个时期6900 s。为了比较，平顶阶段的最后1000毫秒被作为短时间序列截获。当使用短时间序列的数据训练模型并使用同一时期的数据进行测试时，最高AUC会增加到0.9379，训练时间限制为每个纪元36 s。将在短时间序列上训练的最佳模型应用于平顶相进行测试时，AUC最高可达0.9189。实验结果表明，LSTM可以训练机器人。