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Identification of Wiener system with time delay using correlation analysis and swarm intelligence methods.

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Title: Identification of Wiener system with time delay using correlation analysis and swarm intelligence methods.
Authors: Zhang, Yanan1,2 (AUTHOR), Jia, Li1 (AUTHOR) jiali@staff.shu.edu.cn, Li, Feng3 (AUTHOR)
Source: Transactions of the Institute of Measurement & Control. May2026, Vol. 48 Issue 8, p1526-1536. 11p.
Subjects: System identification, Time delay estimation, Fuzzy neural networks, Swarm intelligence, Nonlinear systems, Particle swarm optimization, Statistical correlation
Abstract: In this paper, a novel identification method is addressed for a class of nonlinear Wiener systems subject to time delay, and this identification problem involves the estimation of delay time, transfer function model parameters and neural fuzzy model parameters. Aiming to identify separately the linear and nonlinear blocks, the separable signals are introduced. First, the correlation characteristics of separable signals through the Wiener system are analyzed, then the correlation analysis technique is applied to calculate the unknown parameters involving time delay and transfer function model. Moreover, in the neural fuzzy model parameters estimation, we first calculate the center and width of the neural fuzzy model. Then, to improve global search mechanism ability and converge speed of particle swarm optimization method, the improved particle swarm optimization and cuckoo search techniques are introduced to figure out the weight of the neural fuzzy model, which obtains good global search ability and convergence speed. The simulation comparison results in numerical case and nonlinear process are presented to verify that the feasibility of the Wiener system identification. [ABSTRACT FROM AUTHOR]
Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Identification of Wiener system with time delay using correlation analysis and swarm intelligence methods.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yanan%22">Zhang, Yanan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jia%2C+Li%22">Jia, Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiali@staff.shu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Feng%22">Li, Feng</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Transactions+of+the+Institute+of+Measurement+%26+Control%22">Transactions of the Institute of Measurement & Control</searchLink>. May2026, Vol. 48 Issue 8, p1526-1536. 11p.
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  Data: <searchLink fieldCode="DE" term="%22System+identification%22">System identification</searchLink><br /><searchLink fieldCode="DE" term="%22Time+delay+estimation%22">Time delay estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+neural+networks%22">Fuzzy neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, a novel identification method is addressed for a class of nonlinear Wiener systems subject to time delay, and this identification problem involves the estimation of delay time, transfer function model parameters and neural fuzzy model parameters. Aiming to identify separately the linear and nonlinear blocks, the separable signals are introduced. First, the correlation characteristics of separable signals through the Wiener system are analyzed, then the correlation analysis technique is applied to calculate the unknown parameters involving time delay and transfer function model. Moreover, in the neural fuzzy model parameters estimation, we first calculate the center and width of the neural fuzzy model. Then, to improve global search mechanism ability and converge speed of particle swarm optimization method, the improved particle swarm optimization and cuckoo search techniques are introduced to figure out the weight of the neural fuzzy model, which obtains good global search ability and convergence speed. The simulation comparison results in numerical case and nonlinear process are presented to verify that the feasibility of the Wiener system identification. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/01423312251353252
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1526
    Subjects:
      – SubjectFull: System identification
        Type: general
      – SubjectFull: Time delay estimation
        Type: general
      – SubjectFull: Fuzzy neural networks
        Type: general
      – SubjectFull: Swarm intelligence
        Type: general
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
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      – TitleFull: Identification of Wiener system with time delay using correlation analysis and swarm intelligence methods.
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            NameFull: Zhang, Yanan
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            NameFull: Jia, Li
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            NameFull: Li, Feng
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 48
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            – TitleFull: Transactions of the Institute of Measurement & Control
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