The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Adaptive control of Weiner type nonlinear systems
Automatica (Journal of IFAC)
Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Linear Systems
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
Identification of systems containing linear dynamic and static nonlinear elements
Automatica (Journal of IFAC)
Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models
Automatica (Journal of IFAC)
Decoupling the linear and nonlinear parts in Hammerstein model identification
Automatica (Journal of IFAC)
An approach for identification of uncertain Wiener systems
Mathematical and Computer Modelling: An International Journal
Nonparametric identification of Wiener systems
IEEE Transactions on Information Theory
Mathematics and Computers in Simulation
Hi-index | 0.00 |
Block-oriented models have proved to be useful as simple nonlinear models for a vast number of applications. They are described as a cascade of linear dynamic and nonlinear static blocks. They have emerged as an appealing proposal due to their simplicity and the property of being valid over a larger operating region than a LTI model. In the description of these models, several approaches can be found in the literature to perform the identification process. In this sense, an important improvement is to achieve robust identification of block-oriented models to cope with the presence of uncertainty. In this article, we focus at two special and widely used types of uncertain block-oriented models: Hammerstein and Wiener models. They are assumed to be represented by a parametric representation. The approach herein followed allows to describe the uncertainty as a set of parameters which is found through the solution of an optimization problem. The identification algorithms are illustrated through a set of simple examples.