Abstract
A hybrid method between the Kriging model and the radial basis function (RBF) networks is proposed for robust construction of a response surface of an unknown function. In the hybrid method, RBF approximates the macro trend of the function and the Kriging model estimates the micro trend. Hybrid methods using two types of model selection criteria (MSC), i.e., leave-one-out cross-validation and generalized cross-validation for RBF were applied to three one-dimensional test problems. The results were compared with those of the ordinary Kriging (OK) model and the universal Kriging model. The accuracy of each response surface was compared by function shape and root mean square error. The proposed hybrid models were more accurate than the OK model for highly nonlinear functions because they can capture the macro trend of the function properly by RBF, while the OK model cannot. In addition, the hybrid models can find the global optimum with few sample points using the Kriging model approximation errors.
