Computational algorithm for solving drug pharmacokinetic model under uncertainty with nonsingular kernel type Caputo-Fabrizio fractional derivative

This paper proposes an advanced numerical-analytical approach for handling a class of fuzzy fractional differential equations involving Caputo-Fabrizio derivative with a non-singular kernel arsing in the medical sector. The solution methodology relies on the reproducing-kernel algorithm to generate...

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Vydané v:Alexandria engineering journal Ročník 60; číslo 5; s. 4347 - 4362
Hlavní autori: Harrouche, Nesrine, Momani, Shaher, Hasan, Shatha, Al-Smadi, Mohammed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.10.2021
Elsevier
Predmet:
Caputo-Fabrizio fractional derivative
Fuzzy differential equations
Mathematical modeling of medicine
Pharmacokinetics model
Reproducing kernel algorithm
Stability analysis
Mathematical modeling of medicine
Stability analysis
Pharmacokinetics model
Reproducing kernel algorithm
Fuzzy differential equations
Caputo-Fabrizio fractional derivative
ISSN:1110-0168
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Shrnutí:This paper proposes an advanced numerical-analytical approach for handling a class of fuzzy fractional differential equations involving Caputo-Fabrizio derivative with a non-singular kernel arsing in the medical sector. The solution methodology relies on the reproducing-kernel algorithm to generate analytical solutions in the form of a uniformly convergent series in the direct sum of the desired Hilbert spaces. The effectiveness of the method is analyzed by studying some theoretical, analytical, and stability results of the derived solutions based on the reproducing kernel theory. Numerical simulations are also provided in tables and graphs to demonstrate the reliability of this algorithm in solving fuzzy models using the new Caputo-Fabrizio fractional operator, especially for the drug pharmacokinetic model. The obtained results show the ability of the applied algorithm to solve a wide range of nonlinear fractional models emerging in pharmacology, medicine, and biochemistry.
ISSN:1110-0168
DOI:10.1016/j.aej.2021.03.016