Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

Parametric identification of parallel Wiener-Hammerstein systems

Block-oriented nonlinear models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. To increase the flexibility of single branch block-oriented models, such as Hammerstein, Wiener, and WienerHammerstein models, parallel block-oriented models can be considered. This paper presents a method to identify parallel Wiener-Hammerstein systems...

Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels

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...

On the identification of Hammerstein–Wiener systems

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...

Identification of Wiener–Hammerstein benchmark model via rank minimization

Identification of Hammerstein-Wiener models

This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein–Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and coloured stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method...