Abstract In this work, a new approach to efficient gas radiation characteristics calculating is proposed to satisfy the demand for high accuracy and efficient calculation in many applications. This approach establishes a mapping relationship between gas condition parameters and radiation characteristics using a back-propagation neural network (BPNN). The line by line (LBL) model is utilized for the generation of training samples in the BPNN model. The values of pressure, temperature and component concentration are taken as input, and absorption coefficient values are taken as output. A case study of CO2 transmittance at 2250–2350 cm− 1 band is presented. The comparison and analysis of the results indicated that the BPNN model has a high accuracy of LBL fitting and is insensitive to the input. Although the training time of BPNN is long, once the training is completed, the computational efficiency is very high. Compared to the look-up table method or other accelerated methods using parameter pre-calculation, the BPNN method occupies much less storage space. It can replace the LBL model to a certain extent when dealing with the needs of high precision and high-speed computing.

中文翻译：

---

一种基于人工神经网络的气体辐射特性超快速高精度计算方法

摘要 在这项工作中，为了满足许多应用对高精度和高效计算的需求，提出了一种有效的气体辐射特性计算的新方法。该方法使用反向传播神经网络 (BPNN) 建立气体条件参数和辐射特性之间的映射关系。逐行（LBL）模型用于生成 BPNN 模型中的训练样本。压力、温度和组分浓度值作为输入，吸收系数值作为输出。介绍了 2250-2350 cm-1 波段 CO2 透射率的案例研究。结果对比分析表明，BPNN模型具有较高的LBL拟合精度，对输入不敏感。虽然 BPNN 的训练时间很长，一旦训练完成，计算效率非常高。与查表法或其他使用参数预计算的加速方法相比，BPNN 方法占用的存储空间要少得多。在处理高精度和高速计算的需求时，它可以在一定程度上替代LBL模型。