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Cited by 420

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Modeling attractors of chaotic dynamical systems with fractal–fractional operators

Atangana, Abdon, Qureshi, Sania·2019·Chaos, Solitons & Fractals

Abstract

Abstract In this paper, newly proposed differential and integral operators called the fractal–fractional derivatives and integrals have been used to predict chaotic behavior of some attractors from applied mathematics. These new differential operators are convolution of fractal derivative with power law, exponential decay law with Delta-Dirac property and the generalized Mittag-Leffler function with Delta-Dirac property, respectively. These new operators will be able to capture self-similarities in the chaotic attractors under investigation. We presented, in general, three numerical schemes that can be used to solve such systems of nonlinear differential equations. The general conditions for the existence and the uniqueness of the exact solutions are obtained. These new differential and integral operators applied in five selected chaotic attractors with numerical simulations for varying values of fractional order α and the fractal dimension τ have yielded very interesting results. It is to be believed that this research study will open new doors for in-depth investigation in dynamical systems of varying nature.

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Metadata

Authors
Atangana, A., & Qureshi, S.
Venue
Chaos, Solitons & Fractals
Publisher
Elsevier BV
Published
2019-06
Volume
123
Pages
320-337
ISSN
0960-0779 (ISSN-L)
OpenAlex
W2938708012
Cited by
420
References
8
FWCI
29.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Modeling attractors of chaotic dynamical systems with fractal–fractional operators. https://app.cochat.ai/papers/10.1016/j.chaos.2019.04.020
The paper
Atangana, A., & Qureshi, S. (2019). Modeling attractors of chaotic dynamical systems with fractal–fractional operators. Chaos, Solitons & Fractals. https://doi.org/10.1016/j.chaos.2019.04.020

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.