A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models-Reference-Cited by-同舟云学术

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A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models

Published:2019-02-13 Issue:3 Volume:22 Page:1065-1074
ISSN:1561-8625
Container-title:Asian Journal of Control
language:en
Short-container-title:Asian J Control

Author:

Ghanmi Afef¹²^ORCID,Elloumi Mourad²³^ORCID,Salhi Houda²³,Kamoun Samira²³^ORCID

Affiliation:

1. National Higher Engineering School of Tunis (ENSIT)University of Tunis Tunis Tunisia

2. Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (Lab‐STA)University of Sfax Sfax Tunisia

3. National Engineering School of Sfax (ENIS)University of Sfax Sfax Tunisia

Publisher

Wiley

Subject

Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/asjc.2029

Reference25 articles.

1. Gradient-based iterative parameter estimation for Box–Jenkins systems

2. Recursive identification for Wiener systems using Gaussian inputs

3. Computing an Initial Estimate of a Wiener–Hammerstein System With a Random Phase Multisine Excitation

4. An iterative method for Wiener‐Hammerstein systems paramater identification;Vörös J.;J. Elec. Eng.,2007

5. Recasted models‐based hierarchical extended stochastic gradient method for MIMO nonlinear systems

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1. Parameter learning for Wiener systems with time‐delay state‐space model;Asian Journal of Control;2024-06-04

2. Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals;Frontiers of Information Technology & Electronic Engineering;2024-06

3. Parameter learning for the nonlinear system described by Hammerstein model with output disturbance;Asian Journal of Control;2022-05-10

4. A study on the state and parameter cooperative estimation of nonlinear mechanical systems;Asian Journal of Control;2021-08-19

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