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 …

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 …

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

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 …

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 …

This paper addresses the problem of identifying the graph structure of a dynamical network
using measured input/output data. This problem is known as topology identification and has …

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

Identifiability of a single module in a network of transfer functions is determined by whether a
particular transfer function in the network can be uniquely distinguished within a network …