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
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;
Conference_Titel :
Electrical Engineering and Software Applications (ICEESA), 2013 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4673-6302-0
DOI :
10.1109/ICEESA.2013.6578480
