Recursive identification of Hammerstein systems with dead-zone input nonlinearity

Modeling and parametric identification of Hammerstein systems with time delay and asymmetric dead-zones using fractional differential equations

“…Non-iterative identification methods for Hammerstein models with input dead-zone were proposed in [18,19]. More recently, a gradient based estimation algorithm [20], and a robust recursive algorithm using two improved recursive least squares algorithms [21] have been proposed. It is to be noted that all aforementioned methods were reported for the conventional Hammerstein model i.e.…”

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

“…One class of such nonlinear modeling system is so-called block-oriented models that can be represented in various configurations where linear dynamic blocks and nonlinear static or dynamic subsystems are cascaded. e Hammerstein (H) model (static nonlinear block followed by a dynamic linear one) and the Wiener (W) system (a linear dynamic subsystem followed by a static nonlinear block) are the basic class of the cascaded systems which are widely used in many industrial practice engineering applications [14][15][16][17][18][19][20][21][22] and, therefore, the modeling approaches of such class of block-oriented models have received great attention for many years [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Hammerstein and Wiener systems are combined together to produce more complex subcategories, namely, Hammerstein-Wiener (HW) model (a linear block is cascaded between two nonlinear subsystems) and a Wiener-Hammerstein (WH) system (a nonlinear block is embedded between two linear blocks).…”