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 …

A state‐of‐the‐art review on various identification schemes proposed for the Hammerstein,
Wiener, and Volterra systems is presented with respect to the special problems arising in the …

This article introduces recurrent equilibrium networks (RENs), a new class of nonlinear
dynamical models for applications in machine learning, system identification, and control …

Recent developments within deep learning are relevant for nonlinear system identification
problems. In this paper, we establish connections between the deep learning and the …

This paper deals with the parameter estimation of Hammerstein–Wiener (H–W) nonlinear
systems which have unknown time delay. The linear variable weight particle swarm method …

This paper presents the development of data-driven hybrid nonlinear static-nonlinear
dynamic neural network models and addresses the challenges of optimal estimation of …

This article introduces a network architecture, called dynoNet, utilizing linear dynamical
operators as elementary building blocks. Owing to the dynamical nature of these blocks …

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 …

This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear
dynamical models for applications in machine learning and system identification. The new …