Identification of MIMO Hammerstein models using least squares support vector machines
type: Item
instance of: scholarly article
title: Identification of MIMO Hammerstein models using least squares support vector machines
zbMATH Open document ID: 1086.93064
author: Ivan Goethals
author: Kristiaan Pelckmans
author: Johan A. K. Suykens
author: B. De Moor
published in: Automatica
publication date: 2005-08-05T00:00:00Z
review text: The paper deals with the identification task of SISO and MIMO Hammerstein systems. Linear in the parameters (affine) and ARX model structure corrupted by random disturbances is assumed for the static nonlinearity and linear system dynamics, respectively, and then least squares support vector machines framework is used for determining the unknown nonlinearity and dynamical part parameters from input-output data. Proper computational routines are presented and efficiency of the approach is illustrated by means of simulation examples.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93E25
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 2192834
zbMATH Keywords: Hammerstein system
zbMATH Keywords: support vector machines
zbMATH Keywords: system identification
zbMATH Keywords: kernel methods
zbMATH Keywords: ARX
zbMATH Keywords: LS-SVM
zbMATH Keywords: MIMO
reviewed by: Zygmunt Hasiewicz
MaRDI profile type: Publication
full work available at URL: j.automatica.2005.02.002
OpenAlex ID: W1980759970
cites work: An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
cites work: Q3359644
cites work: Non-parametric orthogonal series identification of Hammerstein systems
cites work: Nonparametric identification of Wiener systems by orthogonal series
cites work: Identification of discrete Hammerstein systems using kernel regression estimates
cites work: Nonparametric identification of a cascade nonlinear time series system
cites work: Q4944206
cites work: The elements of statistical learning. Data mining, inference, and prediction
cites work: Neural network approach for identification of Hammerstein systems
cites work: Identification of discrete Hammerstein systems by the Fourier series regression estimate
cites work: Subspace identification of Hammerstein systems using least squares support vector machines
cites work: Nonlinear black-box modeling in system identification: A unified overview
cites work: Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
cites work: Q4935997
cites work: Identifying MIMO Hammerstein systems in the context of subspace model identification methods
cites work: Q3996207
cites work: Q4217810
DOI: 10.1016/J.AUTOMATICA.2005.02.002
description: scientific article