A possibilistic fuzzy-based Gaussian process regression and its application in nuclear valves

To perform accurate engineering predictions, a method which accounts for both Gaussian process regression (GPR) and possibilistic fuzzy c-means clustering (PFCM) is developed in this paper, where the Gaussian process regression method is used in relationship regressions and the corresponding prediction errors are utilised to determine the memberships of the training samples. On the basis of its memberships and the prediction errors of the clusters, the typicality of each training sample is computed and used to determine the existence of outliers. In actual applications, the identified outliers should be eliminated and predictive model could be developed with the rest of the training samples. In addition to the method of predictive model construction, the influence of key parameters on the model accuracy is also investigated using two numerical problems. The results indicate that compared with standard outlier detection approaches and Gaussian process regression, the proposed approach is able to identify outliers with more precision and generate more accurate prediction results. To further identify the ability and feasibility of the method proposed in this paper in actual engineering applications, a predictive model was developed which can be used to predict the inlet pressure of a nuclear control valve on the basis of its in-situ data. The findings show that the proposed approach outperforms Gaussian process regression. In comparison to the traditional Gaussian process regression, the proposed approach reduces the detrimental impact of outliers and generates a more precise prediction model.