Reduced order infinite impulse response system identification using manta ray foraging optimization

Basit Öğe Kaydı
dc.authoridHerencsar, Norbert/0000-0002-9504-2275
dc.authoridAlagoz, Baris Baykant/0000-0001-5238-6433
dc.authoridYeroglu, Celaleddin/0000-0002-6106-2374
dc.authorwosidHerencsar, Norbert/A-6539-2009
dc.authorwosidAlagoz, Baris Baykant/ABG-8526-2020
dc.contributor.authorMahata, Shibendu
dc.contributor.authorHerencsar, Norbert
dc.contributor.authorAlagoz, Baris Baykant
dc.contributor.authorYeroglu, Celaleddin
dc.date.accessioned2024-08-04T20:54:59Z
dc.date.available2024-08-04T20:54:59Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving the adaptive infinite impulse response (IIR) system identification problem. The effectiveness of the proposed technique is validated on four benchmark IIR models for reduced order system identification. The stability of the proposed estimated IIR system is assured by incorporating a pole-finding and initialization routine in the search procedure of the MRFO algorithm and this algorithmic modification contributes to the MRFO algorithm when seeking stable IIR filter solutions. The absence of such a scheme, which is primarily the case with the majority of the recently published literature, may lead to the generation of an unstable IIR filter for unknown real-world instances (particularly when the estimation order increases). Experiments conducted in this study highlight that the proposed technique helps to achieve a stable filter even though large bounds for the design variables are considered. The convergence rate, robustness, and computational speed of MRFO for all the considered problems are investigated. The influence of the control parameters of MRFO on the design performances is evaluated to gain insight into the interaction between the three foraging strategies of the algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning the design consistency and convergence are conducted for comparison of the proposed MRFO-based approach with six other metaheuristic search procedures to investigate the efficiency. The results on the mean square error metric also highlight the improved solution quality of the proposed approach compared to the various techniques published in the literature.en_US
dc.identifier.doi10.1016/j.aej.2023.12.054
dc.identifier.endpage477en_US
dc.identifier.issn1110-0168
dc.identifier.issn2090-2670
dc.identifier.scopus2-s2.0-85181838738en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage448en_US
dc.identifier.urihttps://doi.org/10.1016/j.aej.2023.12.054
dc.identifier.urihttps://hdl.handle.net/11616/101765
dc.identifier.volume87en_US
dc.identifier.wosWOS:001153626900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofAlexandria Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInfinite impulse response systemen_US
dc.subjectManta ray foraging optimizationen_US
dc.subjectMean square erroren_US
dc.subjectMetaheuristicsen_US
dc.subjectNon-parametric statistical testsen_US
dc.subjectSystem identificationen_US
dc.titleReduced order infinite impulse response system identification using manta ray foraging optimizationen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu