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CLC number: TP273

On-line Access: 2024-07-05

Received: 2023-01-31

Revision Accepted: 2023-07-21

Crosschecked: 2024-07-05

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Feng LI

https://orcid.org/0000-0001-9445-1627

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.6 P.856-868

http://doi.org/10.1631/FITEE.2300058


Separation identification of a neural fuzzy Wiener–Hammerstein system using hybrid signals


Author(s):  Feng LI, Hao YANG, Qingfeng CAO

Affiliation(s):  College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; more

Corresponding email(s):   lifeng@jsut.edu.cn

Key Words:  Wiener–, Hammerstein system, Neural fuzzy network, Correlation analysis technique, Hybrid signals, Separation identification


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.

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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.

基于混合信号的神经模糊Wiener-Hammerstein系统辨识

李峰1,杨浩1,曹晴峰2
1江苏理工学院电气信息工程学院,中国常州市,213001
2扬州大学电气与能源动力工程学院,中国扬州市,225127
摘要:提出一种基于混合信号的神经模糊Wiener-Hammerstein(W-H)系统分离辨识策略。W-H系统由两个线性动态模块和一个非线性静态模块组成。静态非线性模块利用神经模糊网络(NFN)建模,两个线性动态模块分别利用自回归外生(ARX)模型和自回归(AR)模型建模。当系统输入为高斯信号时,利用相关分析技术解耦两个线性动态模块的辨识与非线性模块辨识。首先,基于高斯信号的输入和输出,利用相关分析技术辨识输入线性模块和输出线性模块,解决了W-H系统中间变量信息无法测量的问题。然后,采用零极点匹配方法分离两个线性模块的参数。此外,基于随机信号的输入和输出,利用递归最小二乘法识别非线性模块,避免输出噪声的影响。数值仿真和非线性过程仿真证明了所提辨识技术的可行性。仿真结果表明,所提策略可以获得比现有辨识算法更高的辨识精度。

关键词:Wiener-Hammerstein系统;神经模糊网络;相关分析技术;混合信号;分离辨识

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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