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      Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

      , , , ,
      Biomimetics
      MDPI AG

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          Abstract

          In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

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          Grey Wolf Optimizer

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            Genetic Algorithms

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              Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems

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                Author and article information

                Contributors
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                Journal
                BIOMJE
                Biomimetics
                Biomimetics
                MDPI AG
                2313-7673
                June 2023
                March 30 2023
                : 8
                : 2
                : 141
                Article
                10.3390/biomimetics8020141
                37092393
                6d29802e-a863-4ea4-a36b-36d09a6e3c7c
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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