In this paper, a method for identification of low-order output-error models expressed in terms
of Laguerre basis functions is presented. The identification problem is formulated as a rank …

In system identification we model dynamical systems from measured data. This data-driven
approach to modelling is useful since many real-world systems are difficult to model with …

Recent contributions have tackled the linear system identification problem by means of non-
parametric Bayesian methods, which are built on largely adopted machine learning …

In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on
a VAlidation criterion) estimation error in a general scheme, ie, when the cost function is …

We consider identification of linear systems with a certain order from a set of noisy input-
output observations. We utilize the fact that the system order corresponds to the rank of the …

A method for complexity constrained output-error system identification using rational
orthonormal basis functions is presented. The model is expanded in terms of a Hambo …

The aim of the research embodied in this thesis is to develop robust algorithms in system
identification, ie propose algorithms that can be used to estimate system parameters in a …