Support vector machine modelling applied to benchmark data set for two-phase Coriolis mass flow metering

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.

Automated summary

Using a support vector machine (SVM) approach, this study analyzed a dataset of Coriolis meter errors and found that a linear SVM model outperformed non-linear SVM and neural network models. This improved performance may be attributed to over-fitting by the latter two on the relatively small dataset.