Synchronization of quaternion-valued multi-layer coupled networks: An adaptive activation-time-based event-triggered scheme

IF 6.8 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2025-07-01 Epub Date: 2025-02-20 DOI:10.1016/j.ins.2025.121999
Yue Ren , Haijun Jiang , Cheng Hu , Lianyang Hu , Jiarong Li
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Abstract

This paper develops an adaptive activation-time-based event-triggered scheme (ETS) to investigate the synchronization of quaternion-valued multi-layer coupled networks (QVMLCNs). Firstly, the QVMLCN models with linear coupling and nonlinear coupling are established, respectively. Subsequently, a new adaptive activation-time-based ETS that can only be activated after some particular moments is presented to address the synchronization issue of the considered two types of QVMLCNs. It is worth emphasizing that the principal advantage of the proposed adaptive activation-time-based ETS is that Zeno behavior can be naturally excluded and a positive minimum interevent time can be strictly ensured, which is more resource-conserving than existing adaptive ETSs. Lastly, the usefulness of the theoretical results is verified by some numerical simulations.
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四元数值多层耦合网络的同步:一种基于激活时间的自适应事件触发方案
本文提出了一种基于激活时间的自适应事件触发方案(ETS)来研究四元数值多层耦合网络(QVMLCNs)的同步问题。首先,分别建立了具有线性耦合和非线性耦合的QVMLCN模型。随后,提出了一种新的基于自适应激活时间的ETS,该ETS只能在特定时刻后激活,以解决所考虑的两种QVMLCNs的同步问题。值得强调的是,本文提出的基于自适应激活时间的ETS的主要优点是可以自然地排除Zeno行为,并严格保证正最小事件间时间,这比现有的自适应ETS更节约资源。最后,通过数值模拟验证了理论结果的有效性。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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