A mixed, parametric–non-parametric routine for Hammerstein system identification is presented. Parameters of a non-linear characteristic and of ARMA linear dynamical part of Hammerstein system are estimated by least squares and instrumental variables assuming poor a priori knowledge about the random input and random noise. Both subsystems are identified separately, thanks to the fact that the u...

It is suggested that the differences between the Hammerstein and Wiener models be interpreted and understood in terms of the system eigenvalues. In particular, it is shown that the Wiener representation should be preferred when the system dynamics vary with the operating point. Conversely, when only the system gain varies with the operating point, Hammerstein models generally outperform the Wie...

This work is concerned with identification of Hammerstein systems whose outputs are measured by quantized sensors. The system consists of a memoryless nonlinearity that is polynomial and possibly noninvertible, followed by a linear subsystem. The parameters of linear and nonlinear parts are unknown but have known orders. Input design, identification algorithms, and their essential properties ar...

In this paper a novel class of nonlinear Hammerstein adaptive filters, consisting of a flexible memory-less function followed by a linear combiner, is presented. The nonlinear function involved in the adaptation process is based on a uniform cubic spline function that can be properly modified during learning. The spline control points are adaptively changed by using gradient-based techniques. T...

We are concerning with a nonlinear Hammerstein quadratic integral equation. We prove the existence of at least one positive solution x ∈ L1 under Carathèodory condition. Secondly we will make a link between Peano condition and Carathèodory condition to prove the existence of at least one positive continuous solution. Finally the existence of the maximal and minimal solutions will be proved.

In this work, a weighted least squares (WLS) based adaptive tracker is designed for a class of Hammerstein systems. Incorporating with the diminishing excitation technique, the proposed adaptive tracker leads to the minimality of the tracking errors and strong consistency of the estimates for the unknown system parameters. A numerical example is given and the simulation results are consistent w...

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...

Compactly supported linear semiorthogonal B-spline wavelets together with their dual wavelets are developed to approximate the solutions of nonlinear Fredholm-Hammerstein integral equations. Properties of these wavelets are first presented; these properties are then utilized to reduce the computation of integral equations to some algebraic equations. The method is computationally attractive, an...

We study the numerical solvability of a class of nonlinear weakly singular integral equations of Volterra-Hammerstein type with noncompact kernels. We obtain existence and uniqueness results and analyze the product integration methods for these equations under some verifiable conditions on the kernels and nonlinear functions. The convergence analysis is investigated and finally numerical experi...

We present regularization methods for solving ill-posed Hammerstein type operator equations under weaker conditions than in earlier studies such as [13]. Our semilocal convergence analysis is based on majorizing sequences. Numerical examples where the new convergence criteria are satisfied but the old criteria are not satified are also presented at the end of the study. AMS (MOS) Subject Classi...