Memory-based event-triggered synchronization of dynamic memristor delayed cellular neural networks for image encryption

被引：1
作者：
Luo, Cheng [1 ]
Bao, Haibo [1 ]
Cao, Jinde [2 ]
机构：
[1] Southwest Univ, Sch Math & Stat, Chongqing 400715, Peoples R China
[2] Southeast Univ, Sch Math, Nanjing 210096, Jiangsu, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE | 2025年 / 362卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Dynamic memristor-cellular neural networks; Synchronization; Memory-based event-triggered mechanism (METM); Image encryption; CHAOTIC LURE SYSTEMS; STABILITY;
D O I：
10.1016/j.jfranklin.2025.107552
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper focuses on synchronization issue of dynamic memristor-delayed cellular neural networks (DM-DCNNs) for the first time. Different from the traditional memristor-based NNs (MNNs) that are modeled by discontinuous switched systems, DM-DCNNs where the memristor has flux-controlled and continuous-time nonlinear relation have been paid widespread attention. In order to reduce network burden, a novel memory-based event-triggered mechanism (METM) is proposed to synchronize drive-response systems of DM-DCNNs. With the help of some inequality techniques and Lyapunov stability theory, the sufficient conditions for synchronization are given by some linear matrix inequalities (LMIs). Unlike previous researches, all synchronization results were conducted in the flux-charge domain, which may be a potential advantage for information processing. Then, a numerical example is employed for supporting correctness of these synchronization criteria. Furthermore, the synchronization of DM-DCNNs under METM is further designed as a new type of encryption and decryption algorithms for image protection. Finally, the experimental performances are also provided to verify the high security of designed algorithm with anti-attack capability.
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页数：21
相关论文
共 44 条
  • [1] Chua L., Memristor-the missing circuit element, IEEE Trans. Circuit Theory, 18, 5, pp. 507-519, (1971)
  • [2] Strukov D.B., Snider G.S., Stewart D.R., Williams R.S., The missing memristor found, Nature, 453, 1154, pp. 80-83, (2008)
  • [3] Wu A., Zhang J., Zeng Z., Dynamic behaviors of a class of memristor-based Hopfield networks, Phys. Lett. A, 375, 15, pp. 1661-1665, (2011)
  • [4] He S., Liu J., Wang H., Sun K., A discrete memristive neural network and its application for character recognition, Neurocomputing, 523, pp. 1-8, (2023)
  • [5] Lai Q., Wan Z., Zhang H., Chen G., Design and analysis of multiscroll memristive Hopfield neural network with adjustable memductance and application to image encryption, IEEE Trans. Neural Netw. Learn. Syst., 34, 10, pp. 7824-7837, (2023)
  • [6] Di Marco M., Forti M., Pancioni L., Complete stability of feedback CNNs with dynamic memristors and second-order cells, Int. J. Circuit Theory Appl., 44, 11, pp. 1959-1981, (2016)
  • [7] He W., Luo T., Tang Y., Du W., Tian Y., Qian F., Secure communication based on quantized synchronization of chaotic neural networks under an event-triggered strategy, IEEE Trans. Neural Netw. Learn. Syst., 31, 9, pp. 3334-3345, (2020)
  • [8] Ren H., Ma H., Li H., Wang Z., Adaptive fixed-time control of nonlinear MASs with actuator faults, IEEE/CAA J. Autom. Sin., 10, 5, pp. 1252-1262, (2023)
  • [9] Wu A., Wen S., Zeng Z., Synchronization control of a class of memristor-based recurrent neural networks, Inform. Sci., 183, 1, pp. 106-116, (2012)
  • [10] Wang L., Shen Y., Finite-time stabilizability and instabilizability of delayed memristive neural networks with nonlinear discontinuous controller, IEEE Trans. Neural Netw. Learn. Syst., 26, 11, pp. 2914-2924, (2015)
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