Application of Adaptive Least Square Support Vector Machines in Nonlinear System Identification

Author

Wang, Xiaodong ; Liang, Weifeng ; Cai, Xiushan ; Lv, Ganyun ; Zhang, Changjiang ; Zhang, Haoran

Author_Institution

Coll. of Inf. Sci. & Eng., Zhejiang Normal Univ.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

1897

Lastpage

1900

Abstract

Training problem of least squares support vector machine (LS-SVM) is solved by finding a solution to a set of linear equations. This makes online adaptive implementation of the algorithm feasible. In this paper, an adaptive algorithm for the purpose of nonlinear system identification is proposed. Using this training algorithm, a variant of support vector machine has been developed called adaptive LS-SVM. The adaptive LS-SVM is especially useful on online system identification. Several pertinent numerical simulations have shown the validity of the proposed method

Keywords

adaptive systems; identification; least squares approximations; nonlinear systems; support vector machines; adaptive least square support vector machines; nonlinear system identification; online system identification; Control systems; Educational institutions; Least squares methods; Linear systems; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Support vector machines; System identification; Identification; Nonlinear systems; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712685

Filename

1712685