Abstract
This paper extends the overparametrization technique as used for Hammerstein systems employing nonlinear Least-Squares Support Vector Machines (LS-SVMs) towards the identification of Wiener-Hammerstein systems. We present some practical guidelines as well as empirical results on the performance of the method with respect to various deficiencies of the excitation signal. Finally we apply our method to the SYSID2009 Wiener-Hammerstein benchmark data set.
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