We propose to utilize machine learning to predict the electron density, n_e, and temperature, T_e, from He I line intensity ratios. In this approach, training data consist of measured He I line ratios as input and n_e and T_e measured using other diagnostic(s) as desired output, which is a Langmuir probe in our study. Support vector machine regression analysis is, then, performed with the training data to develop a predictive model for n_e and T_e, separately. It is confirmed that n_e and T_e predicted using the developed models agree well with those from the Langmuir probe in the ranges of 0.28 × 10¹⁸ ≤ n_e (m⁻³) ≤ 3.8 × 10¹⁸ and 3.2 ≤ T_e (eV) ≤ 7.5. The developed models are, further, examined with an evaluation data, which are not included in the training data, and are found to well reproduce absolute values and radial profiles of probe-measured n_e and T_e.

中文翻译：

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从He I线比率对电子密度和温度的机器学习预测

我们建议以利用机器学习来预测电子密度，Ñ _È，和温度，Ť _ê，从他I线的强度比。在这种方法中，训练数据由测得的He I线速比作为输入，以及使用其他诊断程序测得的n _e和T _e作为期望的输出，这是我们研究中的Langmuir探针。然后，使用训练数据执行支持向量机回归分析，以分别开发n _e和T _e的预测模型。确认了n _e和T _e使用开发的模型与在0.28×10的范围为朗缪尔探针吻合预测¹⁸ ≤ Ñ _È（M ^-3）≤3.8×10 ¹⁸和3.2≤ Ť _È（EV）≤7.5。进一步将开发的模型与评估数据一起检查，评估数据不包括在训练数据中，并且可以很好地再现探针测量的n _e和T _e的绝对值和径向轮廓。