A Comparative Study of LS-SVM's Applied to the Silver Box Identification Problem

Within the context of nonlinear system identification, di#erent variants of LS-SVM are applied to the Silver Box dataset. Starting from the dual representation of the LS-SVM, and using Nystrom techniques, it is possible to compute an approximation for the nonlinear mapping to be used in the primal space. In this way, primal space based techniques as Ordinary Least Squares (OLS), Ridge Regression (RR) and Partial Least Squares (PLS) are applied to the same dataset together with the dual version of LS-SVM. We obtain root mean squared error (RMSE) values of the order of 10 using iterative prediction on a pre-defined test set