Detail publikace

Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm

KARTCI, A. AGAMBAYEV, A. FARHAT, M. HERENCSÁR, N. BRANČÍK, L. BAGCI, H. SALAMA, K. N.

Originální název

Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm

Anglický název

Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm

Jazyk

en

Originální abstrakt

This study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately +/- 1 degrees. It provides a phase optimization in the desired bandwidth with minimal branch number and avoids the use of negative component values, and any complex mathematical analysis. Standardized, IEC 60063 compliant commercially available passive component values are used; hence, no correction on passive elements is required. To the best knowledge of the authors, this approach is proposed for the first time in the literature. As validation, we present numerical simulations using MATLAB (R) and experimental measurement results, in particular, the Foster-II and Valsa structures with five branches for precise and/or high-frequency applications. Indeed, the results demonstrate excellent performance and significant improvements over the Oustaloup approximation, the Valsa recursive algorithm, and the continued fraction expansion and the adaptability of the GA-based design with five different types of distributed RC/RL network.

Anglický abstrakt

This study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately +/- 1 degrees. It provides a phase optimization in the desired bandwidth with minimal branch number and avoids the use of negative component values, and any complex mathematical analysis. Standardized, IEC 60063 compliant commercially available passive component values are used; hence, no correction on passive elements is required. To the best knowledge of the authors, this approach is proposed for the first time in the literature. As validation, we present numerical simulations using MATLAB (R) and experimental measurement results, in particular, the Foster-II and Valsa structures with five branches for precise and/or high-frequency applications. Indeed, the results demonstrate excellent performance and significant improvements over the Oustaloup approximation, the Valsa recursive algorithm, and the continued fraction expansion and the adaptability of the GA-based design with five different types of distributed RC/RL network.

Plný text v Digitální knihovně

Dokumenty

BibTex


@article{BUT157018,
  author="Aslihan {Kartci} and Norbert {Herencsár} and Lubomír {Brančík}",
  title="Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm",
  annote="This study proposes a new approach for the optimization of phase and magnitude responses of fractional-order capacitive and inductive elements based on the mixed integer-order genetic algorithm (GA), over a bandwidth of four-decade, and operating up to 1 GHz with a low phase error of approximately +/- 1 degrees. It provides a phase optimization in the desired bandwidth with minimal branch number and avoids the use of negative component values, and any complex mathematical analysis. Standardized, IEC 60063 compliant commercially available passive component values are used; hence, no correction on passive elements is required. To the best knowledge of the authors, this approach is proposed for the first time in the literature. As validation, we present numerical simulations using MATLAB (R) and experimental measurement results, in particular, the Foster-II and Valsa structures with five branches for precise and/or high-frequency applications. Indeed, the results demonstrate excellent performance and significant improvements over the Oustaloup approximation, the Valsa recursive algorithm, and the continued fraction expansion and the adaptability of the GA-based design with five different types of distributed RC/RL network.",
  address="IEEE",
  chapter="157018",
  doi="10.1109/ACCESS.2019.2923166",
  howpublished="online",
  institution="IEEE",
  number="1",
  volume="7",
  year="2019",
  month="june",
  pages="80233--80246",
  publisher="IEEE",
  type="journal article in Web of Science"
}