@unpublished{eprints2429,
           title = {An instrumental Least Squares Support Vector Machine for nonlinear system identification: enforcing zero-centering constraints},
            year = {2013},
          author = {Vincent Laurain and Roland T{\'o}th and Dario Piga},
         address = {The Netherlands},
            note = {TUE-CS-2013-001},
            type = {Technical Report},
     institution = {University of Technology, Eindhoven},
       publisher = {University of Technology, Eindhoven},
           month = {April},
        abstract = {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 ?2 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.},
             url = {http://eprints.imtlucca.it/2429/}
}