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State-Dependent Intermittent Synchronization Control for Coupled Switched Neural Networks: A Prescribed-Time Approach
1
Southwest Guizhou Vocational and Technical College for Nationalities, Xingyi 562400, China
2
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
* Corresponding Author:
Changqing Long,
[email protected]
Volume 1, Issue 1
2025
Received: 30 June 2025
Accepted: 03 August 2025
Published: 24 August 2025
Article Information
Published in
Journal of Nonlinear Dynamics and Applications
Volume/Issue
Volume 1, Issue 1, 2025
Pages
3-9
Cited by
2 (Crossref)
2 (Scopus)
Abstract
This paper addresses the problem of achieving prescribed-time synchronization of coupled switched neural networks (CSNNs) using state-dependent intermittent control. Unlike traditional intermittent control, the intervals for work and rest in this approach are not pre-designed but determined by the relationship between the designed Lyapunov function and the boundary auxiliary functions. The proposed control strategy can effectively mitigate chattering behavior arising from rapid switching in traditional intermittent control. Subsequently, leveraging Lyapunov theory and various inequality techniques, we develop a new set of sufficient conditions, formulated as linear matrix inequalities (LMIs), to ensure prescribed-time synchronization of CSNNs under the designed intermittent control strategy. In the end, a numerical example is given to verify the obtained theoretical results.
Graphical Abstract
Keywords
coupled switched neural networks
intermittent control
prescribed-time synchronization
Lyapunov function
linear matrix inequalities
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.
References
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Cited By (2)
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Mutti-Ur Rehman, Saima Akram, Ali Algefary. Matrix Nearness Problems, D-Stability and μ-Values in Economic Models.
IEEE Access, 2026 , 14 .
[CrossRef] -
Leimin Wang, Zheng Zhou, Guanghui Jiang, Qingyi Wang, Zhouchao Wei. Fixed-/Preassigned-time synchronization of differential-dimensional chaotic systems with stochastic disturbances.
Applied Mathematics and Computation, 2026 , 519 .
[CrossRef]
* Citation data provided by Crossref Cited-by.
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APA Style
Yao, Y., & Long, C. (2025). State-Dependent Intermittent Synchronization Control for Coupled Switched Neural Networks: A Prescribed-Time Approach. Journal of Nonlinear Dynamics and Applications, 1(1), 3–9. https://doi.org/10.62762/JNDA.2025.493957
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TY - JOUR AU - Yao, Yu AU - Long, Changqing PY - 2025 DA - 2025/08/24 TI - State-Dependent Intermittent Synchronization Control for Coupled Switched Neural Networks: A Prescribed-Time Approach JO - Journal of Nonlinear Dynamics and Applications T2 - Journal of Nonlinear Dynamics and Applications JF - Journal of Nonlinear Dynamics and Applications VL - 1 IS - 1 SP - 3 EP - 9 DO - 10.62762/JNDA.2025.493957 UR - https://www.icck.org/article/abs/JNDA.2025.493957 KW - coupled switched neural networks KW - intermittent control KW - prescribed-time synchronization KW - Lyapunov function KW - linear matrix inequalities AB - This paper addresses the problem of achieving prescribed-time synchronization of coupled switched neural networks (CSNNs) using state-dependent intermittent control. Unlike traditional intermittent control, the intervals for work and rest in this approach are not pre-designed but determined by the relationship between the designed Lyapunov function and the boundary auxiliary functions. The proposed control strategy can effectively mitigate chattering behavior arising from rapid switching in traditional intermittent control. Subsequently, leveraging Lyapunov theory and various inequality techniques, we develop a new set of sufficient conditions, formulated as linear matrix inequalities (LMIs), to ensure prescribed-time synchronization of CSNNs under the designed intermittent control strategy. In the end, a numerical example is given to verify the obtained theoretical results. SN - 3069-6313 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Yao2025StateDepen,
author = {Yu Yao and Changqing Long},
title = {State-Dependent Intermittent Synchronization Control for Coupled Switched Neural Networks: A Prescribed-Time Approach},
journal = {Journal of Nonlinear Dynamics and Applications},
year = {2025},
volume = {1},
number = {1},
pages = {3-9},
doi = {10.62762/JNDA.2025.493957},
url = {https://www.icck.org/article/abs/JNDA.2025.493957},
abstract = {This paper addresses the problem of achieving prescribed-time synchronization of coupled switched neural networks (CSNNs) using state-dependent intermittent control. Unlike traditional intermittent control, the intervals for work and rest in this approach are not pre-designed but determined by the relationship between the designed Lyapunov function and the boundary auxiliary functions. The proposed control strategy can effectively mitigate chattering behavior arising from rapid switching in traditional intermittent control. Subsequently, leveraging Lyapunov theory and various inequality techniques, we develop a new set of sufficient conditions, formulated as linear matrix inequalities (LMIs), to ensure prescribed-time synchronization of CSNNs under the designed intermittent control strategy. In the end, a numerical example is given to verify the obtained theoretical results.},
keywords = {coupled switched neural networks, intermittent control, prescribed-time synchronization, Lyapunov function, linear matrix inequalities},
issn = {3069-6313},
publisher = {Institute of Central Computation and Knowledge}
}
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