Block-oriented models are often used to model a nonlinear system. This paper presents an identification method for parallel Wiener-Hammerstein systems, where the obtained model has a decoupled static nonlinear block. This decoupled nature makes the interpretation of the obtained model more easy. First a coupled parallel Wiener-Hammerstein model is estimated. Next, the static nonlinearity is dec...

Necessary and sufficient conditions for dead-beat and complete controllability for a class of generalised Hammerstein systems are presented. Since the system's structure is very close to linear, only linear algebra is used for the controllability test. The test is very simple and easy to use. A closed-loop minimum-time dead-beat controller can be designed for a subclass of generalised Hammerste...

Compensation of nonlinear distortions is an issue of importance for the restoration of degraded audio material. It however remains a very challenging task, especially in cases when only a single instance of the degraded audio signal is available. Compared to other sources of distortion such as additive noise (hiss) or signal gaps, even the fundamental limits achievable by the restoration are ye...

Providing flexibility and user-interpretability in nonlinear system identification can be achieved by means of block-oriented methods. One of such block-oriented system structures is the parallel WienerHammerstein system, which is a sum of Wiener-Hammerstein branches, consisting of static nonlinearities sandwiched between linear dynamical blocks. Parallel Wiener-Hammerstein models have more des...

In this paper, non iterative algorithms for the identification of (multivariable) Hammerstein and Wiener systems are presented. The proposed algorithms are numerically robust, since they are based only on least squares estimation and singular value decomposition. For the Hammerstein model, the algorithm provides consistent estimates even in the presence of coloured output noise, under weak assu...

Hammerstein and Wiener models are nonlinear representations of systems composed by the coupling of a static nonlinearity N and a linear system L in the form N-L and L-N respectively. These models can represent real processes which made them popular in the last decades. The problem of identifying the static nonlinearity and linear system is not a trivial task, and has attracted a lot of research...

Dynamic system identification with Gaussian-process prior model is a probabilistic, nonparametric modelling method for identification. Gaussian-process models provide, besides the prediction, also the information about prediction uncertainty based on the availability or uncertainty of the data used for the modelling. An advantage of this kind of model is a small number of training parameters, a...

In the domain of industrial process modeling and control, Hammerstein model has been used widely to describe a class of nonlinear systems. Goethals et al. (2005) proposed a method based on Least Squares Support Vector Machines (LSSVM) to identify the input-output relationship of the Hammerstein model. Unfortunately, as the data points grow, this kernel learning approach costs much time correspo...

This paper presents a new method for the identification of Hammerstein systems. The parameter estimation problem is formulated as a rank minimization problem by constraining a finite dimensional time dependency between signals. Due to the unknown intermediate signal, the rank minimization problem cannot be solved directly. Thus, the rank minimization problem is reformulated as an intermediate s...

In the paper we recover a Hammerstein system nonlinearity. Hammerstein systems, incorporating nonlinearity and dynamics, play an important role in various applications, and e¤ective algorithms determining their characteristics are not only of theoretical but also of practical interest. The proposed algorithm is quasi-parametric, that is, there are several parametric model candidates and we assu...