Fixed-Time Synchronization of Time-Delayed Memristive Neural Networks With Application to Confidential Communication

XUE Yanbin; TONG Dongbing; CHEN Qiaoyu; MAO Qi

doi:10.21656/1000-0887.450304

Volume 46 Issue 12

Dec. 2025

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Article Navigation > Applied Mathematics and Mechanics > 2025 > 46(12): 1622-1630

XUE Yanbin, TONG Dongbing, CHEN Qiaoyu, MAO Qi. Fixed-Time Synchronization of Time-Delayed Memristive Neural Networks With Application to Confidential Communication[J]. Applied Mathematics and Mechanics, 2025, 46(12): 1622-1630. doi: 10.21656/1000-0887.450304

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Fixed-Time Synchronization of Time-Delayed Memristive Neural Networks With Application to Confidential Communication

doi: 10.21656/1000-0887.450304

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, P.R.China

Funds:

The National Science Foundation of China(61673257)

Received Date: 2024-11-07
Rev Recd Date: 2025-04-18

Available Online: 2025-12-31

Abstract

Abstract

The fixed-time synchronization of time-delayed memristive neural networks and its application in confidential communication was studied. To effectively solve the problem of finite-time synchronization control relying on initial conditions, the Lyapunov stability theory was used to obtain sufficient conditions for fixed-time synchronization of the master-slave system and upper bounds for the settling time through the designed state feedback controller. On this basis, the time-delayed memristive neural network was taken as the transmitter and its response system as the receiver, and the signal encryption was implemented by means of chaotic masking, to enable the recovery of the encrypted signal within a fixed time and ensure the security and timeliness of confidential communication.
- memristive neural network,
- fixed-time synchronization,
- confidential communication,
- Lyapunov stability theory

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References(25)

References

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