Journal of Mechanical Science and Technology

, Volume 23, Issue 4, pp 1175–1181 | Cite as

Comparison study on the accuracy of metamodeling technique for non-convex functions

Article

Abstract

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.

Keywords

Metamodel Moving least squares Kriging Radial basis function Support vector regression 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringHanyang University Graduate schoolSeoulKorea
  2. 2.The Center of Innovative Design Optimization Technology (iDOT)Hanyang UniversitySeoulKorea

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