DocumentCode
2848851
Title
Identification of Wiener Models Using Support Vector Machine
Author
Liang, Hua ; Wang, Bolin
Author_Institution
Coll. of Electr. Eng., Hohai Univ., Nanjing, China
Volume
1
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
511
Lastpage
515
Abstract
The least squares support vector machines (LS-SVM) regression is presented for the purpose of nonlinear dynamic system identification. LS-SVM are used for system identification of Wiener models with memoryless nonlinear blocks and linear dynamical blocks. LS-SVM achieves higher generalization performance. The identification procedure is illustrated using two simulated examples. The results indicate that this approach is effective.
Keywords
identification; least squares approximations; regression analysis; support vector machines; Wiener models; least squares support vector machines regression; linear dynamical blocks; memoryless nonlinear blocks; nonlinear dynamic system identification; Artificial neural networks; Biological system modeling; Educational institutions; Least squares methods; Linear systems; Nonlinear dynamical systems; Parameter estimation; Support vector machine classification; Support vector machines; System identification; Support vector machines; System Identification; Wiener model;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location
Tianjin
Print_ISBN
978-0-7695-3736-8
Type
conf
DOI
10.1109/ICNC.2009.213
Filename
5365250
Link To Document