The present work employs two advanced machine learning (ML) techniques: the Random Forest (RF) model and Deep Neural Network (DNN) for the non-invasive spectroscopic diagnostic of a non-thermal atmospheric pressure Argon plasma jet. By integrating the ML techniques with OES and the collisional radiative (CR) model, real-time prediction of electron temperature was achieved. Both ML models were meticulously optimized by tuning the hyperparameters, employing a random search strategy. An extensive data set was used to train and test both ML models. The DNN showed an impressive R-square value of 0.9964, while the RF model achieved an R-square of 0.9869. These high accuracy levels in predicting , underscore the effectiveness and precision of the combined ML approach. This innovative integration of the RF and DNN models paves the way for an alternate approach to enhance the speed and accuracy of plasma parameter estimation using traditional spectroscopic plasma diagnostic approaches.

中文翻译：

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加速实时等离子体诊断：将氩碰撞辐射模型与机器学习方法相结合

目前的工作采用两种先进的机器学习 (ML) 技术：随机森林 (RF) 模型和深度神经网络 (DNN)，用于非热大气压氩等离子体射流的非侵入性光谱诊断。通过将ML技术与OES和碰撞辐射（CR）模型相结合，实现了电子温度的实时预测。这两个机器学习模型都通过调整超参数并采用随机搜索策略进行了精心优化。使用广泛的数据集来训练和测试这两个机器学习模型。 DNN 显示了令人印象深刻的 R 方值 0.9964，而 RF 模型的 R 方值为 0.9869。这些高精度的预测水平强调了组合机器学习方法的有效性和精确度。 RF 和 DNN 模型的这种创新集成为使用传统光谱等离子体诊断方法提高等离子体参数估计速度和准确性的替代方法铺平了道路。