The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Adaptive control of Weiner type nonlinear systems
Automatica (Journal of IFAC)
On robust stability analysis of a control system using Laguerre series
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
Identifying MIMO Wiener systems using subspace model identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Wiener and Hammerstein uncertain models identification
Mathematics and Computers in Simulation
Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems
Mathematical and Computer Modelling: An International Journal
Identification for the second-order systems based on the step response
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
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As reported in the literature, Wiener models have arisen as an appealing proposal for nonlinear process representation due to their simplicity and their property of being valid over a larger operating region than a LTI model. These models consist of a cascade connection of a linear time invariant system and a static nonlinearity. In the description of these models, there are several ways to represent the linear and the nonlinear blocks, and several approaches can be found in the literature to perform the identification process. In this article, we provide a parametric description for the Wiener system. This approach allows us to describe the uncertainty as a set of parameters. The proposed algorithm is illustrated through a pH neutralization process.