Title :
Support vector machines with composite kernels for nonlinear systems identification
Author :
Gonnouni, Amina El ; Lyhyaoui, Abdelouahid ; Jelali, Soufiane El ; Ramón, Manel Martínez
Author_Institution :
Eng. Syst. Lab.(LIS), Abdelmalek Essaidi Univ., Tangier
Abstract :
In this paper, a nonlinear system identification based on support vector machines (SVM) has been addressed. A family of SVM-ARMA models is presented in order to integrate the input and the output in the reproducing kernel Hilbert space (RKHS). The performances of the different SVM-ARMA formulations for system identification are illustrated with two systems and compared with the least square method.
Keywords :
autoregressive moving average processes; identification; least squares approximations; nonlinear systems; support vector machines; SVM-ARMA models; composite kernels; least square method; nonlinear systems identification; reproducing kernel Hilbert space; support vector machines; Desktop publishing; Hilbert space; Kernel; Least squares methods; Neural networks; Nonlinear systems; Power system modeling; Support vector machine classification; Support vector machines; System identification;
Conference_Titel :
Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on
Conference_Location :
Wisia
Print_ISBN :
978-83-60810-14-9
DOI :
10.1109/IMCSIT.2008.4747226
