An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given i...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2021-01, Vol.2021 (1)
Main Authors: Ben Halima Abid, Donia, Abouda, Saif Eddine, Medhaffar, Hanane, Chtourou, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Autoregressive models
Back propagation
Errors
Filtration
Fuzzy systems
Innovations
Mathematical models
Neural networks
Noise
Nonlinear systems
Nonlinearity
Parameter estimation
Parameter identification
Stochastic systems
Variables
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Summary:This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy-type stochastic output-error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi-innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi-innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi-innovation-based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/8525090