Fixed-Size LS-SVM Applied to the Wiener-Hammerstein Benchmark

Abstract This paper reports on the application of Fixed-Size Least Squares Support Vector Machines (FS-LSSVM) for the identification of the SYSID 2009 Wiener-Hammerstein benchmark data set. The FS-LSSVM is a modification of the standard Support Vector Machine and Least Squares Support Vector Machine (LS-SVM) designed to handle very large data sets. This approach is taken to estimate a nonlinear black-box (NARX) model from given input/output measurements. We indicate how to tune this approach to the specific case study. We obtain a best root mean squared error of 4.7×10 −3 on simulation of the predefined test set.

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