CLC number: TP273
On-line Access: 2024-07-05
Received: 2023-01-31
Revision Accepted: 2023-07-21
Crosschecked: 2024-07-05
Cited: 0
Clicked: 1002
Feng LI, Hao YANG, Qingfeng CAO. Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(6): 856-868.
@article{title="Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals",
author="Feng LI, Hao YANG, Qingfeng CAO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="6",
pages="856-868",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300058"
}
%0 Journal Article
%T Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals
%A Feng LI
%A Hao YANG
%A Qingfeng CAO
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 6
%P 856-868
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300058
TY - JOUR
T1 - Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals
A1 - Feng LI
A1 - Hao YANG
A1 - Qingfeng CAO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 6
SP - 856
EP - 868
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300058
Abstract: A novel separation identification strategy for the neural fuzzy wiener–;hammerstein system using hybrid signals is developed in this study. The wiener–;hammerstein system is described by a model consisting of two linear dynamic elements with a nonlinear static element in between. The static nonlinear element is modeled by a neural fuzzy network (NFN) and the two linear dynamic elements are modeled by an autoregressive exogenous (ARX) model and an autoregressive (AR) model, separately. When the system input is Gaussian signals, the correlation technique is used to decouple the identification of the two linear dynamic elements from the nonlinear element. First, based on the input and output of Gaussian signals, the correlation analysis technique is used to identify the input linear element and output linear element, which addresses the problem that the intermediate variable information cannot be measured in the identified wiener–;hammerstein system. Then, a zero-pole match method is adopted to separate the parameters of the two linear elements. Furthermore, the recursive least-squares technique is used to identify the nonlinear element based on the input and output of random signals, which avoids the impact of output noise. The feasibility of the presented identification technique is demonstrated by an illustrative simulation example and a practical nonlinear process. Simulation results show that the proposed strategy can obtain higher identification precision than existing identification algorithms.
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