Comparison study on the accuracy of metamodeling technique for non-convex functions
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In order to increase the efficiency of design optimization, many efforts have been made on studying the metamodel techniques for effectively representing expensive and complex models. In this study, a comparison is conducted on the accuracy of several widely used meta-model techniques — moving least squares (MLS), Kriging, support vector regression (SVR) and radial basis functions (RBF) — which are able to approximate non-convex functions well. RMSE (root mean squared error) value is identified as a measure of the accuracy for this comparison. Each metamodel technique is used to approximate the six well-known mathematical functions and a resign of experiment (DOE) is generated by using the Latin hypercube design (LHD), which is also performed for each resulting metamodel. The results show that Kriging and MLS can create a more accurate metamodel than SVR and RBF with the mathematical functions tested.
KeywordsMetamodel Moving least squares Kriging Radial basis function Support vector regression
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