首页|基于混合信号的神经模糊Wiener-Hammerstein系统辨识

基于混合信号的神经模糊Wiener-Hammerstein系统辨识

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
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.
Separation identification of a neural fuzzy Wiener-Hammerstein system using hybrid signals
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.

Wiener-Hammerstein systemNeural fuzzy networkCorrelation analysis techniqueHybrid signalsSeparation identification

李峰、杨浩、曹晴峰

展开 >

江苏理工学院电气信息工程学院,中国 常州市,213001

扬州大学电气与能源动力工程学院,中国 扬州市,225127

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

National Natural Science Foundation of ChinaChangzhou Science and Technology BureauChangzhou Science and Technology BureauQinglan Project of Jiangsu Province,ChinaZhongwu Youth Innovative Talents Support Program of Jiangsu University of Technology,China

62003151CJ20220065CM202230152022[29]202102003

2024

10.1631/FITEE.2300058

信息与电子工程前沿(英文)
浙江大学

信息与电子工程前沿(英文)

CSTPCD
影响因子：0.371
ISSN：2095-9184
年,卷(期)：2024.25(6)
  • 1