具有稳定延迟脉冲控制的时滞耦合神经网络的同步。

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2023-10-11 DOI:10.1109/TNNLS.2023.3320651
Lingzhong Zhang, Jianquan Lu, Fengyi Liu, Jungang Lou
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引用次数: 0

摘要

本文研究了具有稳定时滞的脉冲钉扎控制的时滞耦合神经网络的分布式同步问题。提出了一种新的微分不等式,其中有效地提取了状态在脉冲时间的过去信息,并用于处理耦合神经网络的同步。基于该不等式,消除了脉冲延迟大小总是受系统延迟限制的限制,放宽了脉冲延迟的上界,改进了现有的相关结果。利用平均脉冲间隔(AII)和脉冲延迟的方法,得到了时滞耦合神经网络分布式同步的一些松弛准则。所提出的同步条件不影响两个连续脉冲信号的上界,并且下界更灵活。此外,我们的结果表明,脉冲时滞可能有助于时滞系统的同步。最后,给出了典型的网络来说明我们的延迟脉冲控制方法的优点。
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Synchronization of Time-Delay Coupled Neural Networks With Stabilizing Delayed Impulsive Control.

This brief studies the distributed synchronization of time-delay coupled neural networks (NNs) with impulsive pinning control involving stabilizing delays. A novel differential inequality is proposed, where the state's past information at impulsive time is effectively extracted and used to handle the synchronization of coupled NNs. Based on this inequality, the restriction that the size of impulsive delay is always limited by the system delay is removed, and the upper bound on the impulsive delay is relaxed, which is improved the existing related results. By using the methods of average impulsive interval (AII) and impulsive delay, some relaxed criteria for distributed synchronization of time-delay coupled NNs are obtained. The proposed synchronization conditions do not impose on the upper bound of two consecutive impulsive signals, and the lower bound is more flexible. Moreover, our results reveal that the impulsive delays may contribute to the synchronization of time-delay systems. Finally, typical networks are presented to illustrate the advantage of our delayed impulsive control method.

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来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
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