In this work, we describe the use of artificial neural networks (ANNs) for the quantification of x-ray fluorescence measurements. The training data were generated using Monte Carlo simulation, which avoided the use of adapted reference materials. The extension of the available dataset by means of an ANN to generate additional data was demonstrated. Particular emphasis was put on the comparability of simulated and experimental data and how the influence of deviations can be reduced. The search for the optimal hyperparameter, manual and automatic, is also described. For the presented case, we were able to train a network with a mean absolute error of 0.1 weight percent for the synthetic data and 0.7 weight percent for a set of experimental data obtained with certified reference materials.

在这项工作中，我们描述了使用人工神经网络（ANN）量化X射线荧光测量结果。训练数据是使用Monte Carlo模拟生成的，避免了使用经过修改的参考资料。演示了通过ANN扩展可用数据集以生成其他数据的方法。特别强调了模拟数据与实验数据的可比性以及如何减少偏差的影响。还介绍了对最佳超参数（手动和自动）的搜索。对于提出的情况，我们能够训练一个网络，该网络的合成数据的平均绝对误差为0.1重量％，而使用经认证的参考材料获得的一组实验数据的平均绝对误差为0.7重量％。