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• W4313644931 endingPage "107096" @default.
• W4313644931 startingPage "107096" @default.
• W4313644931 abstract "Fractional derivatives are non local operators as they are well-suited for modeling long-memory phenomena such as heat transfers or fluid dynamics. However, such non-locality implies a constant knowledge of the full past of the functions to differentiate. In the context of real-time (online) system identification, such a global property may limit the implementation as calculations can become slower as time grows. Also, instead of using the full data length to compute fractional order derivatives, truncated fractional derivatives are used. However, such a truncation windowing will bring inaccuracy on the computation of fractional derivatives. This study deals with the relationship between the signal frequency content, the fractional derivative truncated approximation as well as the fractional system relaxation in order to get a good approximation of the truncated fractional derivative and consequently, to help providing real-time exploitable system identification algorithms that uses such truncated fractional derivatives." @default.
• W4313644931 created "2023-01-07" @default.
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• W4313644931 date "2023-05-01" @default.
• W4313644931 modified "2023-09-25" @default.
• W4313644931 title "Fractional derivative truncation approximation for real-time applications" @default.
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• W4313644931 doi "https://doi.org/10.1016/j.cnsns.2023.107096" @default.
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