Abstract
In this thesis we discuss subspace identification algorithms for linear, Hammerstein and HammersteinWiener systems. Although linear subspace identification algorithms have been around for several
years, it is shown that under some specific experimental conditions they can break down or yield
unreliable results.
New solutions to known problems involving linear subspace identification and regularization will be
proposed and compared to existing approaches.
In a second part of the thesis, we focus on non-linear subspace identification applied to Hammerstein
and Hammerstein-Wiener systems. By combining ideas from Least Squares Support Vector Machines
with classical subspace identification algorithms for linear systems it is shown that reliable subspace
identification algorithms for Hammerstein and Hammerstein-Wiener systems can be obtained.