Predefined-Time Synchronization of Fractional-Order Delayed Memristor-Based Fuzzy Cellular Neural Networks
- 1School of Finance and Mathematics, Huainan Normal University, 238 Dongshan West Road, Huainan 232038, China
- 2School of Computer Science, Huainan Normal University, 238 Dongshan West Road, Huainan 232038, China
Abstract
In contrast to finite-time and fixed-time synchronization, predefined-time synchronization allows for the convergence time to be explicitly defined as a deterministic value, making it ideal for applications that have stringent timing requirements. This paper addresses the predefined-time synchronization of fractional-order delayed memristive fuzzy cellular neural networks (FDMFCNNs). By leveraging the properties inherent in fractional calculus, we introduce a novel lemma. Based on this lemma and a discontinuous state feedback control method, we derive several innovative criteria for achieving predefined-time synchronization in FDMFCNNs by constructing an appropriate Lyapunov function. The findings presented in this study significantly enhance and broaden the existing literature. Finally, we validate the efficacy of our control strategies for predefined-time synchronization with the help of numerical simulations.
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BibTeX
@article{sun2026predefinedtime,
title = {{Predefined-Time Synchronization of Fractional-Order Delayed Memristor-Based Fuzzy Cellular Neural Networks}},
author = {Yeguo Sun and Yinyin Li and Yihong Liu and Lei Liu},
journal = {Fractal and Fractional},
year = {2026},
month = oct,
doi = {10.3390/fractalfract10100719},
url = {https://www.mdpi.com/2504-3110/10/10/719},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predefined-Time Synchronization of Fractional-Order Delayed Memristor-Based Fuzzy Cellular Neural Networks
AU - Yeguo Sun
AU - Yinyin Li
AU - Yihong Liu
AU - Lei Liu
PY - 2026
DA - 2026/10/13
JO - Fractal and Fractional
DO - 10.3390/fractalfract10100719
UR - https://www.mdpi.com/2504-3110/10/10/719
AB - In contrast to finite-time and fixed-time synchronization, predefined-time synchronization allows for the convergence time to be explicitly defined as a deterministic value, making it ideal for applications that have stringent timing requirements. This paper addresses the predefined-time synchronization of fractional-order delayed memristive fuzzy cellular neural networks (FDMFCNNs). By leveraging the properties inherent in fractional calculus, we introduce a novel lemma. Based on this lemma and a discontinuous state feedback control method, we derive several innovative criteria for achieving predefined-time synchronization in FDMFCNNs by constructing an appropriate Lyapunov function. The findings presented in this study significantly enhance and broaden the existing literature. Finally, we validate the efficacy of our control strategies for predefined-time synchronization with the help of numerical simulations.
SN - 2504-3110
ER -