Abstract Using Artificial Neural Networks (ANN) for nonlinear system identification has
proven to be a promising approach, but despite of all recent research efforts, many practical …

Nonlinear state-space identification for dynamical systems is most often performed by
minimizing the simulation error to reduce the effect of model errors. This optimization …

In this paper, a new approach is developed for the identification of Wiener–Hammerstein
model structures. Unlike several other papers, the transfer functions of linear elements are of …

This paper examines the use of a so-called “generalised Hammerstein–Wiener” model
structure that is formed as the concatenation of an arbitrary number of Hammerstein …

The problem of identifying parametric Wiener–Hammerstein (WH) systems is addressed
within the evolutionary optimisation context. Specifically, a hybrid culture identification …

Deep learning models, such as convolutional neural networks (CNN), have been
successfully applied in pattern recognition and system identification recent years. But for the …

In this work a new initialization scheme for nonlinear state-space models is applied to the
problem of identifying a Wiener–Hammerstein system on the basis of a set of real data. The …

In many engineering domains, eg, high-tech mechatronic systems, water distribution
networks, automotive systems, and even medicine, there is an increasing need to achieve …

In this paper the Wiener–Hammerstein Benchmark is identified as a bilinear discrete system.
The bilinear approximation relies on both facts that the Wiener–Hammerstein system can be …

In this paper, a new identification method is developed for Wiener–Hammerstein systems
that contain memory nonlinearity of backlash type. The latter is flanked by two linear transfer …