Abstract
A novel method, combining Support Vector Machines and Least Squares Prediction Error techniques, for the identification of the linear and nonlinear blocks in a Wiener model is presented in this paper. The identification is carried out by minimizing an augmented cost function defined as the sum of the standard structural risk function appearing in Support Vector Regression and the quadratic criterion on the prediction errors associated to Least Squares estimation methods. The properties of the proposed method are illustrated through simulation examples.
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