The paper deals with identification of Hammerstein and Wiener systems with nonlinearity being a discontinuous piece-wise linear function. Recursive estimation algorithms are given to estimate six unknown parameters contained in the nonlinearity and all coefficients of the linear subsystem. The estimates converge to the corresponding true values with probability one. Numerical examples are given...

Discrete-time modeling (DTM) dominates the systems engineering literature in the applications of block-oriented modeling. The discrete environment of computer-based process control systems and discrete sampling are the two major reasons [1]. Also, a DTM is easier to obtain because all input changes are approximated by piecewise step input sequences. Nonetheless, DTM has (potentially) two critic...

This paper presents a Hammerstein-Wiener recurrent neural network (HWRNN) with a systematic identification algorithm for identifying unknown dynamic nonlinear systems. The proposed HWRNN resembles the conventional Hammerstein-Wiener model that consists of a linear dynamic subsystem that is sandwiched in between two nonlinear static subsystems. The static nonlinear parts are constituted by feedf...

In this paper we present a new approach to modeling dynamic production systems with discrete flow. This method is based on the automatic knowledge domain, in order to build a mathematical model that accurately formalizes the behavior of the studied system. The approach adopted for this study is the parametric identification of nonlinear systems (Hammerstein-Wiener system and NARX model). The pr...

In the note the class of block-oriented dynamic nonlinear systems is considered, in particular, Hammerstein and Wiener systems are investigated. Several algorithms for nonlinear system identification are presented. The algorithms exploit various degrees of prior knowledge from parametric to nonparametric. Eventually, a semiparametric algorithm, which shares advantages of both approaches is anno...

Abstract Hammerstein–Wiener models constitute a significant class of block-structured dynamic models, as they approximate process nonlinearities on the basis input–output data without requiring identification full nonlinear model. Optimization problems with embedded are nonconvex, and thus local optimization methods may obtain suboptimal solutions. In this work, we develop deterministic global ...

hammerstein-wiener (hw) models are capable in describing nonlinear dynamic systems. these models are nonlinear and have been widely used in a wide range of sciences due to their simplicity and having a physically-based concept. in this research, for the first time in hydrology and water resources management, three different structures of these models using daily temperature and precipitation da...