Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms

  • Authors

    • Alaa F. Sheta Texas A&M University-corpus Christi, TX, USA
    • Hossam Faris
    • Ibrahim Aljarah
    2018-07-19
    https://doi.org/10.14419/ijet.v7i3.10.14357
  • Manufacture Process, Meta-Heuristic Search Algorithms, Parameter Estimation

  • Abstract

    This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local optimal. A comparison between Meta-heuristic algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Accelerated PSO, Cuckoo Search, Krill Herd and Firefly algorithm is provided to handle the parameter estimation problem for a Winding process in the industry. The developed ARMA-meta-heuristics models for a winding machine are evaluated based on different evaluation metrics. The results reveal that meta-heuristics can provide an outstanding modeling performance.

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  • How to Cite

    Sheta, A. F., Faris, H., & Aljarah, I. (2018). Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms. International Journal of Engineering & Technology, 7(3.10), 187-194. https://doi.org/10.14419/ijet.v7i3.10.14357

    Received date: 2018-06-19

    Accepted date: 2018-07-17

    Published date: 2018-07-19