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Enhanced cuckoo search algorithm for industrial winding process modeling

Author

Listed:
  • Malik Braik

    (Al-Balqa Applied University)

  • Alaa Sheta

    (Southern Connecticut State University)

  • Heba Al-Hiary

    (Al-Balqa Applied University)

  • Sultan Aljahdali

    (Taif University)

Abstract

Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods.

Suggested Citation

  • Malik Braik & Alaa Sheta & Heba Al-Hiary & Sultan Aljahdali, 2023. "Enhanced cuckoo search algorithm for industrial winding process modeling," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1911-1940, April.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01900-1
    DOI: 10.1007/s10845-021-01900-1
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    References listed on IDEAS

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    1. Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
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