In the paper, machine learning is demonstrated for the diagnostic enhancement of magnetized plasma probes. In the plasma experiment, the magnetic field greatly affects the properties of plasma and the performance of the probe, which limits the range of measurement parameters and reduces the accuracy of probe diagnostic results. Existing probe correction methods based on improved theory and mechanics cannot completely eliminate the influence of magnetic field and often introduce additional errors. In this paper, a novel machine learning method is proposed to improve magnetized plasma probe diagnostic based on existing methods and traditional probe correction theory. The pressure of the plasma, the total voltage of the circuit, and the magnetic induction intensity are used as input parameters, and the electron density obtained from the probe diagnostics are used as output parameters. The paper presents experiments to analyse the original probe results and the probe results revised by magnetic field probe theory through the machine learning algorithm and compare them with the results of theoretical simulations. The experimental results prove that the machine learning model based on revised data has better learning efficiency and prediction results, which can expand the application scope of traditional probe correction theory and predict results closer to the theoretical value.

中文翻译：

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机器学习应用于磁化等离子体诊断的可行性分析

在本文中，机器学习被证明可用于磁化等离子体探针的诊断增强。在等离子体实验中，磁场对等离子体的性质和探头的性能影响很大，限制了测量参数的范围，降低了探头诊断结果的准确性。现有的基于改进的理论和力学的探头校正方法不能完全消除磁场的影响，并且经常引入额外的误差。本文在现有方法和传统探针校正理论的基础上，提出了一种新的机器学习方法来改进磁化等离子体探针的诊断。等离子体的压力、电路的总电压和磁感应强度作为输入参数，从探针诊断中获得的电子密度用作输出参数。文中通过机器学习算法对原始探针结果和磁场探针理论修正后的探针结果进行实验分析，并与理论模拟结果进行比较。实验结果证明，基于修正数据的机器学习模型具有更好的学习效率和预测结果，可以扩大传统探针修正理论的应用范围，预测结果更接近理论值。