作为历史价值加权平均值的卡普托衍生工具:通过 COVID-19 数据说明的一些后果

M. Lopes, Francielle Santo, Maria Beatriz, Ferreira Leite, E. Esmi, L. C. Barros
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引用次数: 0

摘要

.本文采用了作者自己之前提出的公式,其中卡普托分数导数按比例写成经典导数历史值的加权平均值(公式 (2))。本文将讨论该公式的三个结果。第一个结果明确显示了卡普托导数的维度,第二个结果表明了经典导数的哪些历史值对当前时刻的卡普托算子具有更大/更小的权重,最后,第三个结果显示了卡普托导数在临界点发生后的时刻为零(允许对导数的阶次进行解释,例如在某些疾病的动力学中)。为了说明这三个结果,我们使用了作者自己以前获得的例子,用 SIR 模型对活跃的 COVID-19 病例曲线进行建模。这种方法很好地捕捉到了流行病学模型中的记忆效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Caputo Derivative as Weighted Average of Historical Values: some consequences illustrated via COVID-19 data
. This paper uses the formula previously proposed by the authors themselves, in which the Caputo fractional derivative is written proportionally to the weighted average of historical values of the classical derivative (Equation (2)). Three consequences of this formula are treated in this work. The first explicitly shows the dimension of the Caputo derivative, the second indicates which historical values of the classical derivative have greater/lower weight for the Caputo operator at the current instant, and finally, the third shows that the Caputo derivative is zero at instants after the critical point occurred (allowing interpretations for the order of the derivative, for example in the dynamics of some disease). To illustrate these three results, we used examples previously obtained by the authors themselves, modeling the curve of active COVID-19 cases with the SIR model. This approach captures the memory effect well in epidemiological models.
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