Title :
Online identification of nonlinear system using a new kernel algorithm
Author_Institution :
Nat. Sch. of Eng. of Monastir, Univ. of Monastir, Monastir, Tunisia
Abstract :
This paper proposes a new online kernel identification method of a nonlinear system in the Reproducing Kernel Hilbert Space. The proposed algorithm entitled online RKPLS-RN kernel method uses the Reduced Kernel Partial Least Square (RKPLS) technique in an offline phase to construct a RKHS model with reduced parameter number. Then the Regularized Network (RN) method is used on online phase to update the reduced parameters of the RKHS model. The considered measure of performance is the Normalized Means Square Error (NMSE). The proposed online kernel method is evaluated by handling the Cascades Tanks system.
Keywords :
Hilbert spaces; identification; least squares approximations; mean square error methods; nonlinear systems; NMSE; RKPLS technique; RKPLS-RN kernel method; RN method; cascades tanks system; normalized means square error; online kernel identification method; online nonlinear system identification; parameter number; reduced kernel partial least square; regularized network; reproducing kernel Hilbert space; Control systems; Decision support systems; CSTR; Cascades Tanks; Kernel method; Learning machine; online identification;
Conference_Titel :
Systems and Control (ICSC), 2015 4th International Conference on
Conference_Location :
Sousse
Print_ISBN :
978-1-4673-7108-7
DOI :
10.1109/ICoSC.2015.7152789
