In this study, to have an accurate approximation of dynamic viscosity, radial basis function artificial neural network (RBF-ANN) is employed and developed for normal alkanes. This is done by considering the distinct number of carbons in n-alkanes, certain temperatures, and different pressures. Moreover, in order to train and test the predicting model, a databank of 228 experimental data is gathered from reliable sources in the literature. As a result, training and testing coefficient values are measured 0.99739 and 0.99051; consequently, the robustness and accuracy of RBF-ANN in providing an estimation of n-alkane viscosity is confirmed by graphical analysis and determined indexes.

在这项研究中，为了准确地近似动态粘度，采用了径向基函数人工神经网络（RBF-ANN）并开发了用于普通烷烃的方法。这是通过考虑正构烷烃中不同数量的碳，某些温度和不同压力来完成的。此外，为了训练和测试预测模型，从可靠的文献中收集了228个实验数据的数据库。结果，训练和测试系数值测量为0.99739和0.99051；因此，通过图形分析和确定的指标证实了RBF-ANN在提供正构烷烃粘度估算中的鲁棒性和准确性。