A novel, parametric-nonparametric, methodology for Hammerstein system identification is
proposed. Assuming random input and correlated output noise, the parameters of a …

Derived from the idea of stochastic approximation, recursive algorithms to identify a
Hammerstein system are presented. Two of them recover the characteristic of the nonlinear …

A continuous-time Hammerstein system, ie, a system consisting of a nonlinear memoryless
subsystem followed by a linear dynamic one, is identified. The system is driven and …

A continuous-time Hammerstein system driven by a random signal is identified from
observations sampled in time. The sampling may be uniform or not. The a-priori information …

A discrete-time, multiple-input non-linear Hammerstein system is identified. The dynamical
subsystem is recovered using the standard correlation method. The main results concern …

Hammerstein systems are the series composition of a static nonlinear function and a linear
dynamic system. In this work, we propose a nonparametric method for the identification of …

The extended least squares algorithm is applied to identify the Hammerstein system, where
the nonlinear static function f (·) is expressed as a linear combination of basic functions with …

Almost all existing Hammerstein system nonparametric identification algorithms can recover
the unknown system nonlinear element up to an additive constant, and one functional value …

The deterministic identification of Hammerstein systems is investigated in this paper. Based
on the over-sampling technique, a new deterministic identification approach is presented …

This paper presents a new method for the identification of Hammerstein systems. The
parameter estimation problem is formulated as a rank minimization problem by constraining …