relation: http://eprints.imtlucca.it/2429/
title: An instrumental Least Squares Support Vector Machine for nonlinear system identification: enforcing zero-centering constraints
creator: Laurain, Vincent
creator: Tóth, Roland
creator: Piga, Dario
subject: QA75 Electronic computers. Computer science
description: Least-Squares Support Vector Machines (LS-SVM's), originating from Stochastic Learning  theory, represent a promising approach to identify nonlinear systems via nonparametric es-  timation of nonlinearities in a computationally and stochastically attractive way. However,  application of LS-SVM's in the identification context is formulated as a linear regression aim-  ing at the minimization of the ℓ2 loss in terms of the prediction error. This formulation  corresponds to a prejudice of an auto-regressive noise structure, which, especially in the non-  linear context, is often found to be too restrictive in practical applications. In [1], a novel  Instrumental Variable (IV) based estimation is integrated into the LS-SVM approach provid-  ing, under minor conditions, a consistent identification of nonlinear systems in case of a noise  modeling error. It is shown how the cost function of the LS-SVM is modified to achieve an IV-based solution.  In this technical report, a detailed derivation of the results presented in Section 5.2 of [1]  is given as a supplement material for interested readers.
publisher: University of Technology, Eindhoven
date: 2013-04
type: Working Paper
type: NonPeerReviewed
format: application/pdf
language: en
identifier: http://eprints.imtlucca.it/2429/1/Tr012013.pdf
identifier:   Laurain, Vincent and Tóth, Roland and Piga, Dario  An instrumental Least Squares Support Vector Machine for nonlinear system identification: enforcing zero-centering constraints.  Technical Report  #001/2013    University of Technology, Eindhoven , The Netherlands.      (Unpublished)