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Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks
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Zhongjing YU1, Duo ZHANG‡2, Shihan KONG1, Deqiang OUYANG3, Hongfei LI4, Junzhi YU‡1. Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks",
author="Zhongjing YU1, Duo ZHANG‡2, Shihan KONG1, Deqiang OUYANG3, Hongfei LI4, Junzhi YU‡1",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2401000"
}
%0 Journal Article
%T Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks
%A Zhongjing YU1
%A Duo ZHANG‡2
%A Shihan KONG1
%A Deqiang OUYANG3
%A Hongfei LI4
%A Junzhi YU‡1
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2401000
TY - JOUR
T1 - Sum-based dynamic discrete event-triggered mechanism for synchronization of delayed neural networks under deception attacks
A1 - Zhongjing YU1
A1 - Duo ZHANG‡2
A1 - Shihan KONG1
A1 - Deqiang OUYANG3
A1 - Hongfei LI4
A1 - Junzhi YU‡1
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2401000
Abstract: This paper focuses on the design of event-triggered controllers for the synchronization of delayed 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 also suffer from the issues of deception attacks and unmeasurable system states. To enhance the system stability, we adaptively detected 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 utilized 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 introduced a sum-based dynamic discrete event-triggered mechanism (SDDETM), which utilizes a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events. Finally, we incorporated 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 gave an experiment for validating the effectiveness of the algorithm.
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