Aerodynamic Parameter Fitting Based on Robust Least Squares Support Vector Machines

Main advantage of LS-SVM is computationally more efficient than the standard SVM. But unfortunately the sparseness of standard SVM is lost, another problem is that LS-SVM is only optimal if the training samples are corrupted by Gaussian noise. In this paper, a new modified version, robust LS-SVM, which can obtain robust estimates by applying a weighted LS-SVM and achieve the sparseness by doing pruning based upon the sorted support value spectrum, is adopted and introduced into the aerodynamic parameter fitting. The simulation results indicate that robust LS-SVM has a better fitting of aerodynamic parameter with features of simplicity, precision and rapidness to learn.