时变分布时延、泄漏时延和传输时延切换惯性神经网络的精度预分配固定时间同步

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-11-28 DOI:10.1016/j.neucom.2024.128958
Shilei Yuan , Yantao Wang , Xiaona Yang , Xian Zhang
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引用次数: 0

摘要

研究了一类具有时变分布时延、泄漏时延和传输时延的切换惯性神经网络的精度预分配固定时间同步问题。为此,首次提出了一种基于参数化系统解的直接分析方法。与现有的工作不同,该方法从精度预置固定时间同步的定义出发,不需要对惯性项进行变量替换,也不需要构造任何Lyapunov-Krasovskii泛函。这不仅简化了证明过程,而且降低了求解同步条件的计算复杂度。值得注意的是,本文首次将时变泄漏延迟引入切换惯性神经网络。此外,本文中使用的方法与以前实现固定时间同步的所有技术不同。最后,通过数值模拟验证了理论结果的可靠性。
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Accuracy-preassigned fixed-time synchronization of switched inertial neural networks with time-varying distributed, leakage and transmission delays
In this paper, the accuracy-preassigned fixed-time synchronization problem of a class of switched inertial neural networks with time-varying distributed, leakage and transmission delays is studied. To this end, a parameterized system solution-based direct analysis method is proposed for the first time. Unlike existing works, this method sets out from the definition of accuracy-preassigned fixed-time synchronization, and does not require variable substitution for inertial item or the construction of any Lyapunov–Krasovskii functional. This not only simplifies the proof process, but also reduces the computational complexity for solving synchronization conditions. Significantly, this paper introduced the time-varying leakage delay into switched inertial neural networks for the first time. Furthermore, the approach utilized in this manuscript stands apart from all previous techniques for achieving fixed-time synchronization. Finally, the reliability of the theoretical results is verified by numerical simulation.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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