Subspace identification of Hammerstein systems using least squares support vector machines
Authors:
- 10 October 2005
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 50 (10) , 1509-1519
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
This publication has 28 references indexed in Scilit:
- Identification of Hammerstein Models With Cubic Spline NonlinearitiesIEEE Transactions on Biomedical Engineering, 2004
- A blind approach to Hammerstein model identificationIEEE Transactions on Signal Processing, 2002
- Initial estimates of Wiener and Hammerstein systems using multisine excitationIEEE Transactions on Instrumentation and Measurement, 2001
- Identifying MIMO Hammerstein systems in the context of subspace model identification methodsInternational Journal of Control, 1996
- Controller design oriented model identification method for Hammerstein systemAutomatica, 1993
- Subspace algorithms for the stochastic identification problemAutomatica, 1993
- On the series expansion approach to the identification of Hammerstein systemsIEEE Transactions on Automatic Control, 1991
- Adaptive noise cancellation for a class of nonlinear, dynamic reference channelsIEEE Transactions on Circuits and Systems, 1985
- A noniterative method for identification using Hammerstein modelIEEE Transactions on Automatic Control, 1971
- An iterative method for the identification of nonlinear systems using a Hammerstein modelIEEE Transactions on Automatic Control, 1966