In the present study, the machine learning algorithm is utilized for the first time to improve the probe diagnosis. Machine learning methods are utilized to improve the Langmuir probe diagnostic accuracy and the diagnosable plasma parameter range without changing the probe structure based on the Langmuir probe. They provide a new way for experimentally obtaining electron density. A DC glow discharge simulation model and experimental equipment are established. Utilizing the discharge pressure and voltage as independent variables, the simulation and experimental electron densities are collected, the simulation and experimental data are utilized for training, and the plasma electron density outside of the pressure and voltage range of the training data is predicted, thereby achieving the prediction. Simultaneously, when the data amount is large enough, even without experimental measurement, the electron density can be obtained directly through the input parameters, without relying on the plasma physical model.

中文翻译：

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阳性柱诊断的电子密度方法-结合机器学习和Langmuir探针

在本研究中，首次使用机器学习算法来改善探针诊断。利用机器学习方法来提高Langmuir探针的诊断准确性和可诊断的血浆参数范围，而无需更改基于Langmuir探针的探针结构。它们提供了一种通过实验获得电子密度的新方法。建立了直流辉光放电仿真模型和实验设备。利用放电压力和电压作为自变量，收集模拟和实验电子密度，利用模拟和实验数据进行训练，并预测训练数据的压力和电压范围之外的等离子体电子密度，从而实现预测。同时地，