Utilizing Gaussian process regression for nonlinear magnetic separation process identification

• Autorzy: Volovetskyi Oleksandr

Identyfikatory

DOI

10.35784/iapgos.5954

Warianty tytułu

PL

Wykorzystanie regresji procesu gausowskiego do identyfikacji nieliniowych procesów separacji magnetycznej

• Języki publikacji: EN

Abstrakty

EN

This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.

PL

Niniejsza praca prezentuje nowatorskie podejście wykorzystujące regresję procesu Gaussa (Gaussian Process Regression, GPR) do identyfikacji modeli dynamicznych z parametrami nieliniowymi w procesach separacji magnetycznej. Celem jest uwzględnienie złożonego i dynamicznego charakteru tych procesów poprzez zastosowanie zaawansowanych metod modelowania. Skuteczność GPR jest demonstrowana poprzez jego zastosowanie do symulowanych sygnałów, reprezentujących rzeczywiste procesy separacji rudy żelaza, co podkreśla jego potencjał do ulepszania istniejących modeli oraz optymalizacji procesów. Badania przeprowadzone w środowisku MATLAB stanowią podstawę do dalszego rozwoju i praktycznej implementacji. Zastosowanie GPR w separacji magnetycznej pozwala na innowacyjne modelowanie nieliniowych procesów dynamicznych, obiecując poprawę wydajności i precyzji w zastosowaniach przemysłowych.

Słowa kluczowe

EN

Gaussian process regression; magnetic separation; nonlinear modeling; dynamic systems

PL

regresja procesu gaussowskiego; separacja magnetyczna; modelowanie nieliniowe; systemy dynamiczne

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 3
• Strony: 21--28
• Opis fizyczny: Bibliogr. 33 poz., wykr.

Twórcy

autor

Volovetskyi Oleksandr

• Kryvyi Rih National University, Kryvyi Rih, Ukraine

• https://orcid.org/0009-0003-1703-387X

Bibliografia

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