Online LS-SVM Based Nonlinear System Identification

摘要

Least Squares Support Vector Machine (LS-SVM) is an effective method for nonlinear system identification, which is a fundamental topic of control theory. As the conventional training algorithms of LS-SVM are inefficient in online nonlinear system identification, an online learning algorithm is proposed. The online algorithm is suitable for the large data set and practical applications where the data come in sequentially. To illustrate the favorable performance of the online LS-SVM, a nonlinear system identification experiment is considered. The simulation results indicate that the online LS-SVM outperforms conventional LS-SVM with higher efficiency and accuracy of learning.