Title :
On Model Complexity Control in Identification of Hammerstein Systems
Author :
Pelckmans, K. ; Suykens, J.A.K. ; Goethals, I. ; De Moor, B.
Author_Institution :
KULeuven - ESAT - SCD/SISTA, Kasteelpark Arenberg 10, B-3001 Leuven - Belgium. Kristiaan.Pelckmans@esat.kuleuven.ac.be
Abstract :
Model complexity control and regularization play a crucial role in statistical learning theory and also for problems in system identification. This text discusses the potential of the issue of regularization in identification of Hammerstein systems in the context of primal-dual kernel machines and Least Squares Support Vector Machines (LS-SVMs) and proposes an extension of the Hammerstein class to finite order Volterra series and methods resulting in structure detection.
Keywords :
Hammerstein Systems; Identification; Kernel Methods; Model complexity and regularization; Context modeling; Control systems; Kernel; Least squares methods; Machine learning; Predictive models; Qualifications; Statistical learning; Support vector machines; System identification; Hammerstein Systems; Identification; Kernel Methods; Model complexity and regularization;
Conference_Titel :
Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on
Print_ISBN :
0-7803-9567-0
DOI :
10.1109/CDC.2005.1582322
