Title: Identification of MIMO Hammerstein models using least squares support vector machines
Authors: Goethals, Ivan ×
Pelckmans, Kristiaan
Suykens, Johan
De Moor, Bart #
Issue Date: Jul-2005
Publisher: Pergamon-elsevier science ltd
Series Title: Automatica vol:41 issue:7 pages:1263-1272
Abstract: This paper studies a method for the identification of Hammerstein models based on least squares support vector machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. This is done by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples. Another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness. (c) 2005 Elsevier Ltd. All rights reserved.
ISSN: 0005-1098
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering - miscellaneous
× corresponding author
# (joint) last author

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