DocumentCode
2878463
Title
A new online kernel method identification on RKHS space
Author
Taouali, Okba ; Zakraoui, Ines ; Elaissi, Ilyes ; Messaoud, Hassani
Author_Institution
Lab. of Autom. Signal & Image Process., Univ. of Monastir, Monastir, Tunisia
fYear
2013
fDate
21-23 March 2013
Firstpage
1
Lastpage
6
Abstract
This paper proposes a new kernel method for online identification of nonlinear system. The proposed Support Vector Regression-Regularized Network (SVR-RN) method uses the technique SVR in an offline phase to reduce the parameters number of the RKHS. Then the RN method is used to update theses reduced parameters.
Keywords
Hilbert spaces; identification; nonlinear systems; regression analysis; support vector machines; RKHS space; SVR-RN method; nonlinear system; online kernel method identification; reproducing kernel Hilbert space; support vector regression-regularized network method; Data models; Educational institutions; Hilbert space; Kernel; Mathematical model; Measurement uncertainty; Support vector machines; RKHS Kernel method; RN; SVR; Tennessee process; online identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical Engineering and Software Applications (ICEESA), 2013 International Conference on
Conference_Location
Hammamet
Print_ISBN
978-1-4673-6302-0
Type
conf
DOI
10.1109/ICEESA.2013.6578480
Filename
6578480
Link To Document