Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

System identification is a mature research area with well established paradigms, mostly
based on classical statistical methods. Recently, there has been considerable interest in so …

Model estimation and structure detection with short data records are two issues that receive
increasing interests in System Identification. In this paper, a multiple kernel-based …

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

System identification learns mathematical models of dynamic systems starting from input–
output data. Despite its long history, such research area is still extremely active. New …

This paper addresses the problem of obtaining an estimate of a particular module of interest
that is embedded in a dynamic network with known interconnection structure. In this paper it …

Much recent research has dealt with the identifiability of a dynamical network in which the
node signals are connected by causal linear transfer functions and are excited by known …