A Practical Robust and Efficient RBF Metamodel Method for Typical Engineering Problems

Abstract

Radial Basis Function (RBF) metamodels have recently attracted increased interest due to their significant advantages over other types of non-parametric metamodels. However, because of the interpolation nature of the RBF mathematics, the accuracy of the model may dramatically deteriorate if the training data set used contains duplicate information, noise or outliers. Also constructing the metamodel may be time consuming whenever the training data sets are large or a high dimensional model is required. In this paper, we propose a robust and efficient RBF metamodeling approach based on data pre-processing techniques that alleviate the accuracy and efficiency issues commonly encountered when RBF models are used in typical real engineering situations. These techniques include 1) the removal of duplicate training data information, 2) the generation of smaller uniformly distributed subsets of training data from large data sets and 3) the quantification and identification of outliers by principal component analysis (PCA) and Hotelling statistics. Simulation results are used to validate the generalization accuracy and efficiency of the proposed approach.