随机脉冲时滞系统的定时Lyapunov准则及其在Chua电路网络同步中的应用

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-11-22 DOI:10.1016/j.neucom.2024.128943
Xiaofei Xing
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引用次数: 0

摘要

研究了具有延迟脉冲的随机脉冲延迟复杂网络(sicn)的定时稳定性和同步问题。首先,针对具有延迟脉冲的随机时滞系统,建立了一个新的定时稳定性判据,该判据提供了脉冲强度、脉冲瞬间、时滞和稳定时间之间的直接联系;在该判据中,考虑了两种类型的延迟:系统延迟和脉冲延迟,并且每个脉冲时刻的脉冲强度可以不同,(有利于或破坏稳定)。这是第一次考虑这种情况下的固定时间稳定性问题。其次,基于该准则,设计了一种新的反馈控制器和自适应控制器,研究了具有延迟脉冲的sicn的定时同步问题;第三,利用Lyapunov泛函理论、随机分析技术、矩阵不等式分析技术和提出的稳定性判据,以线性矩阵不等式(lmi)的形式导出了sicn的定时同步条件。最后,给出了一个数值算例和蔡氏电路网络的应用实例来说明理论分析的有效性。
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Fixed-time Lyapunov criteria of stochastic impulsive time-delay systems and its application to synchronization of Chua’s circuit networks
In this paper, we investigate the fixed-time stability and synchronization issues of stochastic impulsive delay complex networks (SICNs) with delayed impulses. Firstly, a new fixed-time stability criterion, which provides a direct connection between impulsive strength, impulsive instants, delays and the stability time, is established for stochastic delay systems with delayed impulsive. In this criterion, two types of delays: system delay and impulsive delay are considered, and the impulsive intensity at each impulse instant can be different, (facilitate or disrupt stability). It is the first time to consider the fixed-time stability issues of this case. Secondly, based on this criterion, a novel feedback controller and an adaptive controller are designed to study the fixed-time synchronization of SICNs with delayed impulses; Thirdly, by utilizing Lyapunov functional theory, stochastic analysis technique, matrix inequality analysis techniques and proposed stability criterion, the fixed-time synchronization conditions of SICNs, are derived in the form of linear matrix inequalities (LMIs). Finally, a numerical example and an application example, Chua’s circuit networks, are provided to illustrate the validity of the theoretical analysis.
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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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