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 …

Neural networks and fractional order calculus have shown to be powerful tools for system
identification. In this paper we combine both approaches to propose a fractional order neural …

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 …

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 …

The identification of black-box nonlinear statespace models requires a flexible
representation of the state and output equation. Artificial neural networks have proven to …

By reversing paradigms that normally utilize mathematical models as the basis for nonlinear
adaptive controllers, this article describes using the controller to serve as a novel …

Describing hysteretic systems with a closed-form solution is a challenging task due to some
pitfalls regarding the non-smooth and memory effect mechanisms that do not permit, for …

Structures joined by lap-joints can present complex nonlinear dynamic behavior as a
function of the stress to which the lap-joint is subjected, including contact stiffness variations …

In this paper, the efficient hinging hyperplanes (EHH) neural network is proposed, which is
basically a single hidden layer neural network. Different from the dominant single hidden …