通过混合延迟脉冲实现时延动态网络的同步化

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2024-10-28 DOI:10.1016/j.neunet.2024.106835

Huannan Zheng , Wei Zhu , Xiaodi Li

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引用次数: 0

摘要

本文通过混合延迟脉冲来研究时延动态网络的同步问题，其中同步脉冲和非同步脉冲可以同时发生。本文基于拉祖米金型不等式和 Lyapunov 函数，建立了一些充分同步条件。这些条件对动态网络中的时延大小没有任何限制。具体来说，它可以小于或大于脉冲间隔的长度，与脉冲延迟的大小没有关系。此外，研究结果表明，脉冲延迟对同步有积极作用。两个数值实例证明了理论结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Synchronization of time-delay dynamical networks via hybrid delayed impulses

This paper investigates the synchronization problem of time-delay dynamical networks by means of hybrid delayed impulses, where synchronizing impulses and desynchronizing impulses can occur simultaneously. Some sufficient synchronization conditions are established based on Razumikhin-type inequality and Lyapunov function. These conditions do not place any limitation on the magnitude of time-delay in dynamical networks. To be specific, it can be less than or greater than the length of impulses intervals and has no magnitude relationship with delays in impulses. Moreover, results indicate that delays in impulses have positive contributions to synchronization. The effectiveness of the theoretical results is demonstrated by two numerical examples.

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来源期刊

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.