基于神经网络的EAST-NBI实验数据分析系统研究

Research on EAST-NBI experimental data analysis system based on neural networks

NBI离子源的运行参数不匹配会导致离子源中的等离子体不稳定,并可能导致离子源打火,从而限制了NBI长脉冲和高功率的运行。为了稳定NBI离子源中的等离子体,使用来自多轮实验的数据以及从预测性等离子体模型获得的先验信息,针对现有的实验数据值提取算法精度不高的问题,采用OPTICS聚类算法提高了实验数据提取的准确性,为实验操作过程中的故障分析提供参考。同时采用了一种基于自组织特征映射图(SOM)和逆向传播(BP)神经网络的方法,通过训练历史数据,在给定参数下估算NBI离子源束引出过程中的脉冲宽度,以调整运行参数。

The mismatch of the operation parameters of the NBI(neutral beam injection)ion source of the nuclear fusion experimental device will lead to the instability of the plasma in the ion source,and maybe cause breakdown in the ion source,which will limit the long pulse and high power operation of the NBI.To stabilize the plasma in the ion source,data from multiple campaigns of experiments along with a prior information obtained from a predictive plasma model are used.For the problem of poor accuracy of the existing key-values extraction algorithms for experimental data,the OPTICS(Ordering Point To Identify the Cluster Structure)algorithm is used to improve the accuracy of the experimental data extraction,and provide a reference for the failure analysis during the experimental operation.A method based on Self-Organizing feature Map(SOM)and Back Propagation(BP)neural network is adopted.By training historical data,the pulse width of the NBI ion source beam extraction process is estimated under given parameters to adjust the operating parameters.