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Wyświetlanie 1-2 z 2
Tytuł:
Equivalent diagrams of fractional order elements
Autorzy:
Różowicz, Sebastian
Włodarczyk, Maciej
Zawadzki, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/27324011.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
fractional order derivative
Laplace transform for fractional order systems
CFE method
circuit synthesis
numerical experiments
Opis:
This paper presents equivalent impedance and operator admittance systems for fractional order elements. Presented models of fractional order elements of the type: sαL,sub>α and 1/sαCsub>α, (0 α 1) were obtained using the Laplace transform based on the expansion of the factor sign to an infinite fraction with varying degrees of accuracy – the continued fraction expansion method (CFE). Then circuit synthesis methods were applied. As a result, equivalent circuit diagrams of fractional order elements were obtained. The obtained equivalent schemes consist both of classical RLC elements, as well as active elements built based on operational amplifiers. Numerical experiments were conducted for the constructed models, presenting responses to selected input signals.
Źródło:
Archives of Control Sciences; 2023, 33, 4; 801--827
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Long-term Traffic Forecasting in Optical Networks Using Machine Learning
Autorzy:
Walkowiak, Krzysztof
Szostak, Daniel
Włodarczyk, Adam
Kasprzak, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/27311948.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
traffic forecasting
machine learning
classification
Regression
Opis:
Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-terT traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 751--762
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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