Twin Support Vector Machine Method for Identification of Wiener Models

Abstract

Twin support vector regression is applied to identify nonlinear Wiener system, consisting of a linear dynamic block in series with static nonlinearity. The linear block is expanded in terms of basis functions, such as Laguerre or Kautz filters, and the static nonlinear block is determined using twin support vector machine regression. Simulation of a control valve model and pH neutralization process have been presented to show the features of the proposed algorithm over support vector machine based algorithm.