Scholarlink

<p>This research proposes the Oppositional Mayfly Algorithm (OMA) to design reliable, stable, and wideband Fractional-Order Digital Integrators and Differentiators (FODIs/FODDs). The need for supercomputing develops from the "many-design" problem: realizing a family of high-accuracy, various order FODI/FODD models for real-time signal processing applications involve a comprehensive search of a high-dimensional, non-linear solution space. OMA is uniquely suited for this HPC task, revealing superior efficiency and robustness by incorporating parallel fitness computation on distributed systems. This parallel scalability enables the rapid convergence to optimal designs that would be too resource-intensive for standard workstations. By realizing an outstanding convergence rate, frequency response, and solution precision, OMA empowers the accelerated optimization of complex filter designs on baseline machines or promotes the addressing of even high-dimensional challenges on established HPC frameworks, thereby expediting studies in computer-based physics and instantaneous control system mechanisms.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimal identification of IIR-type fractional-order digital integrator and differentiator using a meta-heuristic optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年12月27日

查看全文
  • Souvik Dey,
  • Provas Kumar Roy,
  • Angsuman Sarkar

摘要

This research proposes the Oppositional Mayfly Algorithm (OMA) to design reliable, stable, and wideband Fractional-Order Digital Integrators and Differentiators (FODIs/FODDs). The need for supercomputing develops from the "many-design" problem: realizing a family of high-accuracy, various order FODI/FODD models for real-time signal processing applications involve a comprehensive search of a high-dimensional, non-linear solution space. OMA is uniquely suited for this HPC task, revealing superior efficiency and robustness by incorporating parallel fitness computation on distributed systems. This parallel scalability enables the rapid convergence to optimal designs that would be too resource-intensive for standard workstations. By realizing an outstanding convergence rate, frequency response, and solution precision, OMA empowers the accelerated optimization of complex filter designs on baseline machines or promotes the addressing of even high-dimensional challenges on established HPC frameworks, thereby expediting studies in computer-based physics and instantaneous control system mechanisms.