Title :
Nonlinear System Identification Using Least Squares Support Vector Machine
Author :
Liang, Hua ; Song, Jinya ; Wang, Bolin
Author_Institution :
Hohai Univ., Nanjing
Abstract :
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.
Keywords :
identification; least squares approximations; nonlinear control systems; nonlinear dynamical systems; support vector machines; actuator saturation; least squares support vector machine; nonlinear system identification; Actuators; Artificial neural networks; Control systems; Least squares methods; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Support vector machine classification; Support vector machines; System identification;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.505
