In this paper, a bias-correction least-squares (LS) algorithm is proposed for identifying block-
oriented errors-in-variables nonlinear Hammerstein (EIV-Hammerstein) systems. Because …

This study presents a modified Kalman filtering-based multi-step-length gradient iterative
algorithm to identify ARX models with missing outputs. The Kalman filtering method is …

In this article, a gray-box parsimonious subspace identification method using sampled
dynamical and steady-state data is proposed for block-oriented Hammerstein-type systems …

For the regression problem of support vector machine, the solution processes of the most
existing methods use offline datasets, which cannot be realized online. For this problem, this …

Abstract The Hammerstein–Wiener model is a nonlinear system with three blocks where a
dynamic linear block is sandwiched between two static nonlinear blocks. For parameter …

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

This paper is concerned with robust identification of Wiener systems in the presence of
dynamic disturbances and stochastic noises. Since conventional statistical method cannot …

This paper mainly investigates the issue of parameters identification for continuous time
fractional order Hammerstein systems. When the commensurate order of linear part in …

A novel parameter learning scheme using multi-signals is proposed for estimating
parameters of the Hammerstein nonlinear model in this research. The Hammerstein …

In this study, a novel approach for nonlinear process identification via neural fuzzy-based
Hammerstein-Wiener model with process disturbance by means of multi-signal processing …