A robust ensemble surrogate model and its application in the in situ data modelling and analysis of a combine harvester

In this article, a robust ensemble model is proposed based on extended adaptive hybrid functions and fuzzy clustering. In the outlier detection stage, each sample is assigned memberships to judge whether it is an outlier or not, where the memberships are determined based on the responses of the ensemble surrogate model of each cluster. Then, the detected outliers are removed from the initial training samples, and the final prediction model is constructed based on the remaining normal samples. The results of numerical problems and the in-situ dataset from a combine harvester show that the proposed model can provide accurate detection results for outliers and accurate prediction results for new points. The sensitivity analysis based on the proposed robust ensemble model indicates that the angle of guide plate, the open rate of cleaning fan, and the height of header have a greater effect on the cleaning loss of combine harvester.