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 …

The problem of identifying dynamical models on the basis of measurement data is usually
considered in a classical open-loop or closed-loop setting. In this paper, this problem is …

Dynamic networks are structured interconnections of dynamical systems (modules) driven
by external excitation and disturbance signals. In order to identify their dynamical properties …

This article presents a systematic algorithm to select a set of auxiliary measurements in
order to consistently identify certain transfer functions in a dynamic network from …

The identification of local modules in dynamic networks with known topology has recently
been addressed by formulating conditions for arriving at consistent estimates of the module …

The problem of identifying sparse solutions for the link structure and dynamics of an
unknown linear, time-invariant network is posed as finding sparse solutions x to A x= b. If the …

This article addresses the problem of consistently identifying a single transfer function in a
network of dynamic systems using only observational data. It is assumed that the topology is …

We study discrete time dynamical systems governed by the state equation $ h_ {t+ 1}=\phi
(Ah_t+ Bu_t) $. Here $ A, B $ are weight matrices, $\phi $ is an activation function, and $ u_t …

This paper is concerned with the problem of distributed Kalman filtering in a network of
interconnected subsystems with distributed control protocols. We consider networks, which …

Dynamic networks are structured interconnections of dynamical systems driven by external
excitation and disturbance signals. We develop the notion of network identifiability, a …