Predefined-Time Synchronization Control of Fractional Cohen-Grossberg Neural Networks with Non-Identical Fractional Orders under Time-Varying Delays

Chaouki Aouiti; El Abed Assali

doi:10.62762/JNDA.2025.975574

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Volume 1, Issue 2, Journal of Nonlinear Dynamics and Applications

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Journal of Nonlinear Dynamics and Applications, Volume 1, Issue 2, 2025: 99-111

Free to Read | Research Article | 16 December 2025

Predefined-Time Synchronization Control of Fractional Cohen-Grossberg Neural Networks with Non-Identical Fractional Orders under Time-Varying Delays

Chaouki Aouiti 1

El Abed Assali 1,2 *

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]

DOI: 10.62762/JNDA.2025.975574

Received: 08 October 2025, Accepted: 12 December 2025, Published: 16 December 2025

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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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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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