A known challenge when building nonlinear models from data is to limit the size of the
model in terms of the number of parameters. Especially for complex nonlinear systems …

Abstract Models play an important role in many engineering fields. Therefore, the goal in
system identification is to find the good balance between the accuracy, complexity and …

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 …

In this paper a dedicated recurrent neural network design and a model reduction approach
are proposed in order to improve the balance between complexity and quality of black box …

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

Nonlinear system identification tends to provide highly accurate models these last decades;
however, the user remains interested in finding a good balance between high-accuracy …

Fractional systems are known to model complex dynamics with a reduced number of
parameters. This paper deals with identification of discrete fractional order systems based …

In this paper, two nonlinear optimization methods for the identification of nonlinear systems
are compared. Both methods estimate the parameters of eg a polynomial nonlinear state …

The application of neural networks to non-linear dynamic system identification tasks has a
long history, which consists mostly of autoregressive approaches. Autoregression, the usage …

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 …