Title :
Subspace identification of Hammerstein systems using least squares support vector machines
Author :
Goethals, Ivan ; Pelckmans, Kristiaan ; Suykens, Johan A K ; De Moor, Bart
Author_Institution :
Dept. of Electr. Eng. ESAT-SCD, Katholieke Univ. Leuven, Belgium
Abstract :
This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems. The linear model and static nonlinearities follow from a low-rank approximation of a matrix obtained from this regression problem.
Keywords :
MIMO systems; control nonlinearities; identification; least squares approximations; linear systems; regression analysis; state-space methods; support vector machines; Hammerstein system; N4SID; least squares support vector machines; linear model; multiple input multiple output system; numerical algorithms; regression problem; static nonlinearities; subspace state space identification; Biological processes; Biological system modeling; Least squares approximation; Least squares methods; MIMO; Nonlinear systems; Signal processing algorithms; State-space methods; Support vector machines; System identification; Hammerstein models; least squares support vector machines; subspace identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2005.856647
