An earlier paper introduced a dataset of Coriolis meter mass flow and density errors, induced by the effects of two-phase (gas/liquid) flow, as a benchmark for which various error correction strategies might be developed. That paper further presented a series of error correction models based on neural nets. The current paper presents an alternative analysis of the same data set, using a support vector machine (SVM) approach. The analysis demonstrates that, for the benchmark data set, more accurate models are generated than those developed using neural nets. More specifically, it is found that a linear SVM model provides better performance than non-linear SVM. This improved performance may arise from over-fitting by both non-linear SVM and neural nets on this relatively small data set.

较早的一篇论文介绍了由两相（气/液）流影响引起的科里奥利仪表质量流量和密度误差数据集，作为可以开发各种误差校正策略的基准。该论文进一步提出了一系列基于神经网络的纠错模型。当前论文提出了使用支持向量机 (SVM) 方法对同一数据集进行的另一种分析。分析表明，对于基准数据集，生成的模型比使用神经网络开发的模型更准确。更具体地说，发现线性 SVM 模型提供比非线性 SVM 更好的性能。这种改进的性能可能源于非线性 SVM 和神经网络在这个相对较小的数据集上的过度拟合。