Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

Regularization and Bayesian methods for system identification have been repopularized in
the recent years, and proved to be competitive wrt classical parametric approaches. In this …

In this article, a unified identification framework called constrained subspace method for
structured state-space models (COSMOS) is presented, where the structure is defined by a …

This paper considers the identification of a network consisting of discrete-time LTI systems
that are interconnected by their unmeasurable states. For a large-scale network, the …

Kernel-based regularization approaches have been successfully applied in the last years for
regression purposes. Recently, these machine learning techniques have been also …

Inspired by ideas taken from the machine learning literature, new regularization techniques
have been recently introduced in linear system identification. In particular, all the adopted …

Subspace identification techniques have gained widespread acceptance as a method of
obtaining a low-order model from data. These are based on using the singular-value …

The problem of low-rank approximation with convex constraints, which appears in data
analysis, system identification, model order reduction, low-order controller design, and low …

Noncausal finite impulse response (FIR) models are used for closed-loop identification of
unstable multi-input, multi-output plants. These models are shown to approximate the …

Kernel-based regularization approaches for impulse response estimation of Linear Time-
Invariant (LTI) systems have received a lot of attention recently. The reason is that …