Nonlinear Dynamic System Identification into Wiener Model using Subspace Identification and Support Vector Machine

In this article, a new blockoriented nonlinear identification method is proposed. This modeling method uses the Wiener model comprised of a linear dynamic block that is followed by a nonlinear static block. The linear block is described by the subspace identification algorithm whereas the nonlinear one is represented via the Least SquaresSupport Vector Machine. The proposed method is tested with a practical nonlinear chemical plant named as CSTR. A dataset of the input-output signals gathered from the system is applied to show the superiority of the method.