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CLC number: TP273

On-line Access: 2025-10-13

Received: 2024-11-12

Revision Accepted: 2025-02-23

Crosschecked: 2025-10-13

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Junzhi YU

https://orcid.org/0000-0002-6347-572X

Zhongjing YU

https://orcid.org/0000-0003-4128-5877

Duo ZHANG

https://orcid.org/0000-0003-4895-2636

Shihan KONG

https://orcid.org/0000-0002-6714-1313

Deqiang OUYANG

https://orcid.org/0000-0003-2259-886X

Hongfei LI

https://orcid.org/0000-0002-9816-717X

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Frontiers of Information Technology & Electronic Engineering  2025 Vol.26 No.9 P.1662-1678

http://doi.org/10.1631/FITEE.2401000


Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks


Author(s):  Zhongjing YU, Duo ZHANG, Shihan KONG, Deqiang OUYANG, Hongfei LI, Junzhi YU

Affiliation(s):  State Key Laboratory for Turbulence and Complex Systems, School of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China; more

Corresponding email(s):   yuzhongjing@pku.edu.cn, duozhang92@std.uestc.edu.cn, junzhi.yu@ia.ac.cn

Key Words:  Sum-based dynamic discrete event-triggered mechanism, Takagi–, Sugeno (T–, S) fuzzy model, Deception attacks


Zhongjing YU, Duo ZHANG, Shihan KONG, Deqiang OUYANG, Hongfei LI, Junzhi YU. Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1662-1678.

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journal="Frontiers of Information Technology & Electronic Engineering",
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Abstract: 
This paper focuses on the design of event-triggered controllers for the synchronization of delayed takagi–;sugeno (T–;S) fuzzy neural networks (NNs) under deception attacks. The traditional event-triggered mechanism (ETM) determines the next trigger based on the current sample, resulting in network congestion. Furthermore, such methods suffer from the issues of deception attacks and unmeasurable system states. To enhance the system stability, we adaptively detect the occurrence of events over a period of time. In addition, deception attacks are recharacterized to describe general scenarios. Specifically, the following enhancements are implemented: First, we use a Bernoulli process to model the occurrence of deception attacks, which can describe a variety of attack scenarios as a type of general Markov process. Second, we introduce a sum-based dynamic discrete event-triggered mechanism (SDDETM), which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events. Finally, we incorporate a dynamic output feedback controller (DOFC) to ensure the system stability. The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization (CCL) algorithm. We further perform two simulation examples to validate the effectiveness of the algorithm.

基于和值的动态离散事件触发机制在遭受欺骗攻击的时滞神经网络同步中的应用

于忠靖1,张舵2,孔诗涵1,欧阳德强3,李洪飞4,喻俊志1
1北京大学工学部先进制造与机器人学院,湍流与复杂系统全国重点实验室,中国北京市,100871
2智能博弈与决策实验室,中国北京市,100097
3重庆大学计算机学院,中国重庆市,400044
4西南大学电子信息工程学院,中国重庆市,400715
摘要:本文聚焦于欺骗攻击环境下时滞T-S模糊神经网络同步的事件触发控制器设计。传统事件触发机制(ETM)依据当前采样点确定下一次触发,易导致网络拥塞,且存在欺骗攻击和系统状态不可测的问题。为增强系统稳定性,我们采用自适应检测机制在特定时间段内识别事件发生,同时重新刻画欺骗攻击以涵盖一般场景。具体改进如下:首先,利用伯努利过程建模欺骗攻击发生机制,将其作为广义马尔可夫过程描述多种攻击场景;其次,引入基于和值的动态离散事件触发机制(SDDETM),该机制结合历史采样测量值和内部动态变量来确定后续触发事件;最后,整合动态输出反馈控制器(DOFC)以确保系统稳定性。通过应用锥补线性化(CCL)算法,实现了DOFC与SDDETM参数的协同设计。通过两个仿真算例验证了该算法的有效性。

关键词:基于和值的动态离散事件触发机制;T-S模糊模型;欺骗攻击

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