Fractional-order system identification: efficient reduced-order modeling with particle swarm optimization and AI-based algorithms for edge computing applications

Vista sencilla de ítem
dc.contributor.authorFidalgo Astorquia, Ignacio
dc.contributor.authorGomez Larrakoetxea, Nerea
dc.contributor.authorGude, Juan José
dc.contributor.authorPastor López, Iker
dc.date.accessioned2025-06-18T09:55:36Z
dc.date.available2025-06-18T09:55:36Z
dc.date.issued2025-04-16
dc.date.updated2025-06-18T09:55:36Z
dc.description.abstractFractional-order systems capture complex dynamic behaviors more accurately than integer-order models, yet their real-time identification remains challenging, particularly in resource-constrained environments. This work proposes a hybrid framework that combines Particle Swarm Optimization (PSO) with various artificial intelligence (AI) techniques to estimate reduced-order models of fractional systems. First, PSO optimizes model parameters by minimizing the discrepancy between the high-order system response and the reduced model output. These optimized parameters then serve as training data for several AI-based algorithms—including neural networks, support vector regression (SVR), and extreme gradient boosting (XGBoost)—to evaluate their inference speed and accuracy. Experimental validation on a custom-built heating system demonstrates that both PSO and the AI techniques yield precise reduced-order models. While PSO achieves slightly lower error metrics, its iterative nature leads to higher and more variable computation times compared to the deterministic and rapid inference of AI approaches. These findings highlight a trade-off between estimation accuracy and computational efficiency, providing a robust solution for real-time fractional-order system identification on edge devices.en
dc.description.sponsorshipThis work was conducted within the GRECO project, “Transformation of AI systems engineering to improve efficiency and environmental impact through GREen COmputing”, funded by the SPRI (Basque Business Development Agency) under the ELKARTEK program, grant number KK-2024/0090en
dc.identifier.citationFidalgo Astorquia, I., Gómez-Larrakoetxea, N., Gude, J. J., & Pastor, I. (2025). Fractional-order system identification: efficient reduced-order modeling with particle swarm optimization and AI-based algorithms for edge computing applications. Mathematics, 13(8). https://doi.org/10.3390/MATH13081308
dc.identifier.doi10.3390/MATH13081308
dc.identifier.eissn2227-7390
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3080
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2025 by the authors
dc.subject.otherArtificial intelligence
dc.subject.otherEdge computing
dc.subject.otherFractional-order systems
dc.subject.otherParticle Swarm Optimization
dc.subject.otherReal-time control
dc.subject.otherReduced-order modeling
dc.titleFractional-order system identification: efficient reduced-order modeling with particle swarm optimization and AI-based algorithms for edge computing applicationsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue8
oaire.citation.titleMathematics
oaire.citation.volume13
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
fidalgo_fractional_2025.pdf
Tamaño:
1.8 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos