Nonlinear System Identification Using Least Squares Support Vector Machine

The least squares support vector machines (LS-SVMs) regression is presented for the purpose of nonlinear dynamic system identification. LS-SVMs are used for system identification of system models with static nonlinear part and dynamic linear part. The actuator saturation is a common nonlinear problem in practical control systems. The identification procedure is illustrated using a simulated example. Model obtained using this method is accurate to be implemented in a model based control algorithm. The results indicate that this approach is effective.