In this article, a consistent subspace identification method (SIM) is proposed for block-
oriented errors-in-variables Hammerstein systems. Due to that the existing SIMs using parity …

The optimization and monitoring of the energy consumption of machinery lead to a
sustainable and efficient industry. For this reason and following a digital twin strategy, an …

The aim of this book is to show that the nonparametric regression can be successfully
applied to system identification and how much can be achieved in this way. It gathers what …

Although linear principal component analysis (PCA) originates from the work of Sylvester
[67] and Pearson [51], the development of nonlinear counterparts has only received …

We use Koopman theory to develop a data-driven nonlinear model reduction and
identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical …

The existing results show the applicability of the over-parameterized model based subspace
identification method (OPM-like SIM) developed for consistent estimates of Hammerstein …

Iterative learning control (ILC) relies on a finite-time interval output predictor to determine the
output trajectory in each trial. Robust ILCs intend to model the uncertainties in the predictor …

Aiming at the difficult identification of fractional order Hammerstein nonlinear systems,
including many identification parameters and coupling variables, unmeasurable …

In this study, neuro‐fuzzy‐based identification procedure for Hammerstein model with
coloured noise is presented. Separable signal is used to realise the decoupling of the …

In this paper, a neuro-fuzzy based identification procedure for Hammerstein output error
model with colored noise is presented. Separable signal is used to realize the decoupling of …