Identification of Nonlinear Dynamic Systems Using Fuzzy Hammerstein-Wiener Systems

In this paper, a new fuzzy Hammerstein-Wiener model (FHWM) is developed in order to identify a nonlinear dynamic system operating in a stochastic environment. Wherein more general aspect is considered like both non-invertible nonlinearities and stochastic disturbances before the Wiener nonlinearity....

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Bibliographic Details
Main Authors: Abouda, Saif Eddine, Ben Halima Abid, Donia, Elloumi, Mourad, Koubaa, Yassine, Chaari, Abdessattar
Format: Conference Proceeding
Language:English
Subjects:
BPG algorithm
Estimation
Fuzzy Hammerstein-Wiener model
Gradient methods
Hammerstein Wiener mathematical model
Mathematical model
Nonlinear dynamical systems
nonlinear stochastic system
Numerical models
Stochastic processes
Takagi-Sugeno (T-S) fuzzy models
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Summary:In this paper, a new fuzzy Hammerstein-Wiener model (FHWM) is developed in order to identify a nonlinear dynamic system operating in a stochastic environment. Wherein more general aspect is considered like both non-invertible nonlinearities and stochastic disturbances before the Wiener nonlinearity. The FHWM consists of a linear dynamic subsystem surrounded by two static Takagi-Sugeno (T-S) fuzzy models. The Back Propagation based Gradient method (BPG) is used to determine jointly the parameters and the internal variable of the proposed FHWM. A numerical example is provided to demonstrate the performance of the FHWM.
ISSN:2573-539X
DOI:10.1109/STA.2019.8717218