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Wiener–Hammerstein system identification – an evolutionary approach

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  • Abdessamad Naitali
  • Fouad Giri

Abstract

The problem of identifying parametric Wiener–Hammerstein (WH) systems is addressed within the evolutionary optimisation context. Specifically, a hybrid culture identification method is developed that involves model structure adaptation using genetic recombination and model parameter learning using particle swarm optimisation. The method enjoys three interesting features: (1) the risk of premature convergence of model parameter estimates to local optima is significantly reduced, due to the constantly maintained diversity of model candidates; (2) no prior knowledge is needed except for upper bounds on the system structure indices; (3) the method is fully autonomous as no interaction is needed with the user during the optimum search process. The performances of the proposed method will be illustrated and compared to alternative methods using a well-established WH benchmark.

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  • 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.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:1:p:45-61
    DOI: 10.1080/00207721.2015.1027758
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    Cited by:

    1. 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.

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