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 …

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 …

This paper describes a new algorithm for initializing and estimating Wiener–Hammerstein
models which consist of two linear parts with a static nonlinearity in between. The algorithm …

This work presents the identification of a Wiener-Hammerstein system by a learning-from-
examples approach, namely the Support Vector Machines for Regression, on the basis of a …

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 …

The parameters of a Wiener-Hammerstein model, a nonlinear block structure comprising two
linear filters separated by a memoryless nonlinearity, may be identified using an iterative …

A convergence analysis is performed for a recursive prediction error algorithm based on
nonlinear ODEs and the midpoint integration algorithm. Several conditions are formulated …

This paper describes a new algorithm for initializing and estimating Wiener-Hammerstein
models. The algorithm makes use of the best linear model of the system which is split in all …