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

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dc.contributor.authorMahata, Shibenducs
dc.contributor.authorHerencsár, Norbertcs
dc.contributor.authorAlagoz, Baris Baykantcs
dc.contributor.authorYeroglu, Celaleddincs
dc.coverage.issue1cs
dc.coverage.volume87cs
dc.date.issued2024-01-05cs
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
dc.formattextcs
dc.format.extent448-477cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationAlexandria Engineering Journal. 2024, vol. 87, issue 1, p. 448-477.en
dc.identifier.doi10.1016/j.aej.2023.12.054cs
dc.identifier.issn2090-2670cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other186919cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttp://hdl.handle.net/11012/244197
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofAlexandria Engineering Journalcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1110016823011468cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2090-2670/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectInfinite impulse response systemen
dc.subjectManta ray foraging optimizationen
dc.subjectMean square erroren
dc.subjectMetaheuristicsen
dc.subjectNon-parametric statistical testsen
dc.subjectSystem identificationen
dc.titleReduced order infinite impulse response system identification using manta ray foraging optimizationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-186919en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:34en
sync.item.modts2025.01.17 16:54:53en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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