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Predefined-Time Synchronization Control of Fractional Cohen-Grossberg Neural Networks with Non-Identical Fractional Orders under Time-Varying Delays
1
Department of Mathematics, Faculty of Sciences of Bizerte, University of Carthage, Bizerte 7021, Tunisia
2
Higher School of Engineering of Medjez el Bab (ESIM), University of Jendouba, Medjez el Bab, Tunisia
* Corresponding Author:
El Abed Assali,
[email protected]
Volume 1, Issue 2
2025
Received: 08 October 2025
Accepted: 12 December 2025
Published: 16 December 2025
Article Information
Published in
Journal of Nonlinear Dynamics and Applications
Volume/Issue
Volume 1, Issue 2, 2025
Pages
99-111
Cited by
4 (Crossref)
4 (Scopus)
Abstract
In this paper, we study the problem of predefined-time synchronization for distinct-order fractional delayed Cohen-Grossberg neural networks. Fractional-order models are known for their ability to capture memory effects and complex dynamics more accurately than classical integer-order systems. In particular, allowing distinct-order in the drive and response systems provides additional flexibility in modeling. To achieve synchronization, we propose two control strategies that provide sufficient conditions for predefined-time synchronization of the addressed model. These strategies are based on the construction of an appropriate Lyapunov function and the use of fractional calculus properties. Finally, two numerical examples are provided to verify the effectiveness of the proposed methods.
Graphical Abstract
Keywords
fractional-order
neural networks
predefined-time
synchronization
control
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Aouiti, C., & Assali, E. A. (2025). Predefined-Time Synchronization Control of Fractional Cohen-Grossberg Neural Networks with Non-Identical Fractional Orders under Time-Varying Delays. Journal of Nonlinear Dynamics and Applications, 1(2), 99–111. https://doi.org/10.62762/JNDA.2025.975574
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TY - JOUR AU - Aouiti, Chaouki AU - Assali, El Abed PY - 2025 DA - 2025/12/16 TI - Predefined-Time Synchronization Control of Fractional Cohen-Grossberg Neural Networks with Non-Identical Fractional Orders under Time-Varying Delays JO - Journal of Nonlinear Dynamics and Applications T2 - Journal of Nonlinear Dynamics and Applications JF - Journal of Nonlinear Dynamics and Applications VL - 1 IS - 2 SP - 99 EP - 111 DO - 10.62762/JNDA.2025.975574 UR - https://www.icck.org/article/abs/JNDA.2025.975574 KW - fractional-order KW - neural networks KW - predefined-time KW - synchronization KW - control AB - In this paper, we study the problem of predefined-time synchronization for distinct-order fractional delayed Cohen-Grossberg neural networks. Fractional-order models are known for their ability to capture memory effects and complex dynamics more accurately than classical integer-order systems. In particular, allowing distinct-order in the drive and response systems provides additional flexibility in modeling. To achieve synchronization, we propose two control strategies that provide sufficient conditions for predefined-time synchronization of the addressed model. These strategies are based on the construction of an appropriate Lyapunov function and the use of fractional calculus properties. Finally, two numerical examples are provided to verify the effectiveness of the proposed methods. SN - 3069-6313 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Aouiti2025Predefined,
author = {Chaouki Aouiti and El Abed Assali},
title = {Predefined-Time Synchronization Control of Fractional Cohen-Grossberg Neural Networks with Non-Identical Fractional Orders under Time-Varying Delays},
journal = {Journal of Nonlinear Dynamics and Applications},
year = {2025},
volume = {1},
number = {2},
pages = {99-111},
doi = {10.62762/JNDA.2025.975574},
url = {https://www.icck.org/article/abs/JNDA.2025.975574},
abstract = {In this paper, we study the problem of predefined-time synchronization for distinct-order fractional delayed Cohen-Grossberg neural networks. Fractional-order models are known for their ability to capture memory effects and complex dynamics more accurately than classical integer-order systems. In particular, allowing distinct-order in the drive and response systems provides additional flexibility in modeling. To achieve synchronization, we propose two control strategies that provide sufficient conditions for predefined-time synchronization of the addressed model. These strategies are based on the construction of an appropriate Lyapunov function and the use of fractional calculus properties. Finally, two numerical examples are provided to verify the effectiveness of the proposed methods.},
keywords = {fractional-order, neural networks, predefined-time, synchronization, control},
issn = {3069-6313},
publisher = {Institute of Central Computation and Knowledge}
}
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