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2010
IEEE

Linear parametric noise models for Least Squares Support Vector Machines

9 years 6 months ago
Linear parametric noise models for Least Squares Support Vector Machines
In the identification of nonlinear dynamical models it may happen that not only the system dynamics have to be modeled but also the noise has a dynamic character. We show how to adapt Least Squares Support Vector Machines (LSSVMs) to take advantage of a known or unknown noise model. We furthermore investigate a convex approximation based on overparametrization to estimate a linear auto regressive noise model jointly with a model for the nonlinear system. Considering a noise model can improve generalization performance. We discuss several properties of the proposed scheme on synthetic data sets and finally demonstrate its applicability on real world data.
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where CDC
Authors Tillmann Falck, Johan A. K. Suykens, Bart De Moor
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