System identification based on Hammerstein model

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

“…In particular, the nonlinear subsystem often has a predetermined linear in the parameters model structure. The special structure of Hammerstein models can be exploited to develop hybrid parameter estimation algorithms [3,9,17]. It has been shown that the Bernstein basis used in Bezier curve is the best conditioned and the most stable among any other polynomial basis [18].…”

“…Various approaches have been developed in order to capture the a priori unknown nonlinearity by use of both parametric [8,9] and nonparametric methods [6,7,16]. In the parametric approaches the unknown nonlinear function is restricted by some parametric representation with a finite number of parameters.…”

“…The Hammerstein model, comprising a nonlinear static functional transformation followed by a linear dynamical model, has been widely researched [1][2][3][4][5][6][7][8][9]. It is a popular nonlinear plant/ process modelling approach for a wide range of biological/ engineering problems [10][11][12][13].…”

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

“…For example, it is a suitable model for signal processing applications involving any nonlinear distortion followed by a linear filter, the modelling of the human heart in order to regulate the heart rate during treadmill exercises (Su 2007), and the modelling of hydraulic actuator friction dynamics (Kwak, Yagle, and Levitt 1998). The Hammerstein model has been widely researched (Billings and Fakhouri 1979;Stoica and So¨derstro¨m 1982;Greblicki and Pawlak 1986;Greblicki 1989Greblicki , 2002Verhaegen and Westwick 1996;Lang 1997;Bai and Fu 2002;Chen 2004;Chaoui, Giri, Rochdi, Haloua, and Naitali 2005). The model characterisation/representation of the unknown nonlinear static function is fundamental to the identification of Hammerstein model.…”

PSO Assisted NURB Neural Network Identification

“…The model characterization/representation of the unknown nonlinear static function is fundamental to the identification of Hammerstein model. Various approaches have been developed in order to capture the a priori unknown nonlinearity by use of both parametric [22,6] and nonparametric methods [17,11,7]. The special structure of Hammerstein models can be exploited to develop hybrid parameter estimation algorithms [2,1,6].…”