The convergence theorems for the stochastic approximation (SA) algorithm with expanding truncations are first presented, which the system identification methods discussed in the paper are essentially based on. Then, the recursive identification algorithms are respectively defined for the multivariate errors-in-variables systems, Hammerstein systems, and Wiener systems. All estimates given in th...

The prevailing analyses of sinusoidally perturbed extremum-seeking (ES) schemes require the ES scheme to act slowly relative to the plant dynamics. Existing analyses that do allow for fast ES are fairly restrictive, considering only local behaviour on Wiener–Hammerstein plants where the nonlinear element is quadratic. In this paper, semi-global stability of an ES scheme acting on a general Hamm...

Special classes of nonlinear systems applied in engineering are nonlinear systems with both block-oriented Hammerstein and Wiener structures, respectively [1, 3, 4, 7, 8, 14]. There are a lot of papers devoted to the different aspects of the parametric identification of Hammerstein and Wiener systems and much less on that of the Hammerstein–Wiener (H-W) systems with so-called hard nonlinearitie...

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...

　A new approach is introduced to estimate the formal information of neurons. Formal Information, mainly discusses about the aspects of the response that is related to the stimulus. Estimation is based on introducing a mathematical nonlinear model with Hammerstein-Wiener system estimator. This method of system identification consists of three blocks to completely describe the nonlinearity of inp...

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...

This study provides a recursive parametric identification scheme for liquid-saturated steam heat exchanger system. The uses block-structured Wiener and Hammerstein models as model structure least squares estimation the parameter method. estimated block-oriented higher accuracy of than linear provided in literature. From simulation results, it is observed that can provide 88% goodness-of-FIT, wh...