Title :
Identification of non linear system modeled in Reproducing Kernel Hilbert Space using a new criterion
Author :
N. Souilem;I. Elaissi;O. Taouali;M. Hassani
Author_Institution :
Laboratoirr de Recherche d´Automatique, Traitement de Signal et Image (LARATSI), Ecole Nationale d´Ing?nieur Monastir
Abstract :
This paper proposes a new algorithm to estimate the required number of parameters in the models developed in Reproducing Kernel Hilbert Space (RKHS). The proposed method considers models with growing complexities and calculates for each a given matrix, such that these matrices tend to singularity. The required number of parameters is given by verifying a criterion on the determinants of these matrices.
Keywords :
"Kernel","Biological system modeling","Hilbert space","Convergence","Complexity theory","Training","Eigenvalues and eigenfunctions"
Conference_Titel :
Computer Vision and Image Analysis Applications (ICCVIA), 2015 International Conference on
Print_ISBN :
978-1-4799-7185-5
DOI :
10.1109/ICCVIA.2015.7351900