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WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

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  • J. Zambrano
  • J. Sanchis
  • J. M. Herrero
  • M. Martínez

Abstract

Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

Suggested Citation

  • J. Zambrano & J. Sanchis & J. M. Herrero & M. Martínez, 2018. "WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification," Complexity, Hindawi, vol. 2018, pages 1-17, February.
    • Handle: RePEc:hin:complx:1753262
    DOI: 10.1155/2018/1753262
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    References listed on IDEAS

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    1. Abdessamad Naitali & Fouad Giri, 2016. "Wiener–Hammerstein system identification – an evolutionary approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(1), pages 45-61, January.
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    Cited by:

    1. Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2022. "Fuzzy-weighted differential evolution computing paradigm for fractional order nonlinear wiener systems," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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