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

Zhongjing YU; Duo ZHANG; Shihan KONG; Deqiang OUYANG; Hongfei LI; Junzhi YU

doi:10.1631/FITEE.2401000

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

Regular Papers | Updated：2025-11-05

- Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 9, Pages: 1662-1678(2025)
- Affiliations：
  
  1.State Key Laboratory for Turbulence and Complex Systems, School of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
  2.Intelligent Game and Decision Laboratory, Beijing 100097, China
  3.College of Computer Science, Chongqing University, Chongqing 400044, China
  4.College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
- Author bio：
  
  E-mail: yuzhongjing@pku.edu.cn
  ‡ Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2401000
  CLC： TP273
- Received：12 November 2024，
  
  Revised：2025-02-23，
  
  Published：2025-09
- Accepted：
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Zhongjing YU, Duo ZHANG, Shihan KONG, et al. 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.
DOI：

Zhongjing YU, Duo ZHANG, Shihan KONG, et al. 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. DOI： 10.1631/FITEE.2401000.

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。

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.

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references

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