Efficient design of wideband digital fractional order differentiators and integrators using multi-verse optimizer

被引:14
|
作者
Ali, Talal Ahmed Ali [1 ,2 ]
Xiao, Zhu [1 ]
Mirjalili, Seyedali [3 ]
Havyarimana, Vincent [1 ,4 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
[2] Taiz Univ, Coll Engn & Informat Technol, Taizi, Yemen
[3] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimisat, Brisbane, Qld 4006, Australia
[4] Ecole Normale Suprieure Bujumbura, Dept Appl Sci, Bujumbura 6983, Burundi
基金
中国国家自然科学基金;
关键词
Digital fractional order differentiator; Digital fractional order integrator; Particle Swarm Optimization; Multi-verse optimizer; Genetic Algorithm; Metaheuristic; Optimization; L-1-norm; SERIES EXPANSION; FIR; DOMAIN; POWER; APPROXIMATIONS;
D O I
10.1016/j.asoc.2020.106340
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a novel method is proposed based on combining L-1-norm optimally criterion with a recently-proposed metaheuristic called multi-verse optimizer (MVO) to design 2nd-4th order stable, minimum phase and wideband infinite impulse response (IIR) digital fractional order differentiators (DFODs) for the fractional order differentiators (FODs) of one-half, one-third and one-fourth order. To confirm the superiority of the proposed approach, we conduct comparisons of the MVO-based designs with the real-coded genetic algorithm (RCGA) and particle swarm optimization (PSO)-based designs in terms of accuracy, robustness, consistency, and efficiency. The transfer functions of the proposed designs are inverted to obtain new models of digital fractional order integrators (DFOIs) of the same order. A comparative study of the frequency responses of the proposed digital fractional order differentiators and integrators with the ones of the existing models is then conducted. The results demonstrate that the proposed designs yield the optimal magnitude responses in terms of absolute magnitude error (AME) with flat response profiles. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
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