As a powerful tool to solve nonlinear problems, artificial neural network method (ANN) gets a wide range of applications in data regression. However, the overfitting often occurs during the ANN training process, which results in high accuracy of correlating the training data but poor prediction performance. At the same time, the principle of k-Nearest Neighbor method (kNN) makes it impossible to make an accurate prediction exceeding the range of the training data, but it can confine the overfitting of ANN. In this work, combining ANN and kNN, a new machine learning method called ANN-kNN combination method (AKC) for thermophysical property prediction of material is proposed. To evaluate the performance of AKC, we take the thermophysical properties of natural gas as an example. The inputs of AKC are temperature, pressure and the components of natural gas, the outputs are the compressibility factor, speed of sound and viscosity. AKC not only overcomes the overfitting problem but also needs less training data than ANN. The average absolute relative deviation of AKC for prediction are 2.5%, which are better than ANN (5.9%) and kNN (19.2%).

作为解决非线性问题的有力工具，人工神经网络方法（ANN）在数据回归中得到了广泛的应用。然而，在人工神经网络训练过程中经常会发生过拟合，导致训练数据的相关精度高，但预测性能较差。同时，k-最近邻法（kNN）的原理使得超出训练数据范围无法做出准确预测，但可以限制ANN的过拟合。在这项工作中，结合ANN和kNN，提出了一种新的机器学习方法，称为ANN-kNN组合方法（AKC），用于材料的热物理特性预测。为了评估AKC的性能，我们以天然气的热物理特性为例。AKC 的输入是温度，压力和天然气的成分，输出是压缩系数、声速和粘度。AKC 不仅克服了过拟合问题，而且比 ANN 需要更少的训练数据。AKC预测的平均绝对相对偏差为2.5%，优于ANN（5.9%）和kNN（19.2%）。