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<p>This research work presents the realization of a decisive, substantial, and wideband infinite impulse response-type fractional order digital differentiator (FODD) adopting a new nature-influenced meta-heuristic optimization algorithm, namely quasi-chaotic opposition-based mayfly algorithm (QCOMA). QCOMA substantially behaves better than some famous techniques like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), flower pollination algorithm (FPA), hybrid flower pollination algorithm (HFPA), and mayfly algorithm (MA) in terms of various magnitude error behaviors, interpretation aspect precision, convergence nature, and computational time needed to determine the optimal explanation. In this research proposal, the authors intensively analyze the convergence behavior and magnitude error performances of the various order FODD’s by RGA, PSO, DE, FPA, HFPA, MA, and QCOMA. MATLAB simulation outcomes also strongly justify the magnitude response efficiency and stability of the offered various order FODD’s over some currently published research articles.</p>

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Infinite impulse response based reliable fractional order digital differentiator identification using an evolutionary optimization approach

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Souvik Dey,
  • Provas Kumar Roy,
  • Angsuman Sarkar

摘要

This research work presents the realization of a decisive, substantial, and wideband infinite impulse response-type fractional order digital differentiator (FODD) adopting a new nature-influenced meta-heuristic optimization algorithm, namely quasi-chaotic opposition-based mayfly algorithm (QCOMA). QCOMA substantially behaves better than some famous techniques like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), flower pollination algorithm (FPA), hybrid flower pollination algorithm (HFPA), and mayfly algorithm (MA) in terms of various magnitude error behaviors, interpretation aspect precision, convergence nature, and computational time needed to determine the optimal explanation. In this research proposal, the authors intensively analyze the convergence behavior and magnitude error performances of the various order FODD’s by RGA, PSO, DE, FPA, HFPA, MA, and QCOMA. MATLAB simulation outcomes also strongly justify the magnitude response efficiency and stability of the offered various order FODD’s over some currently published research articles.