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 …

Block-oriented nonlinear models are popular in nonlinear system identification because of
their advantages of being simple to understand and easy to use. Many different identification …

This paper describes a time-evolving digital twin and its application to a proof-of-concept
engineering dynamics example. In this work, the digital twin is constructed by combining …

We consider a nonlinear state–space model with the state transition and observation
functions expressed as basis function expansions. The coefficients in the basis function …

Deep neural networks have become very popular in modeling complex nonlinear processes
due to their extraordinary ability to fit arbitrary nonlinear functions from data with minimal …

This paper presents a record of the participation of the authors in a workshop on nonlinear
system identification held in 2016. It provides a summary of a keynote lecture by one of the …

Volterra series approximate a broad range of nonlinear systems. Their identification is
challenging due to the curse of dimensionality: the number of model parameters grows …

Nonlinear system identification is a fast evolving field of research with contributions from
different communities. It is not always straightforward to compare different models and …

Identification of Hammerstein systems finds extensive applications in solving the stability
and design issues of non-linear dynamic systems. Hence, in this paper, a first attempt is …

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for
system identification. Although DNNs show impressive approximation ability in various …