Finite-time synchronization of proportional delay memristive competitive neural networks

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-09-16 DOI:10.1016/j.neucom.2024.128612
Jiapeng Han, Liqun Zhou
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引用次数: 0

Abstract

The finite-time synchronization (FTS) is considered for proportional delay memristive competitive neural networks (PDMCNNs). By utilizing Lyapunov functional method and differential inclusion theory, two new criteria ensuring the FTS of PDMCNNs are established. These criteria with algebraic inequality forms are less complicated and easier to verify than the matrix inequality forms. In addition, the corresponding settling times have been estimated. Eventually, the effectiveness of the presented criteria and controllers is confirmed through two numerical examples, and one application about image encryption is provided.
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比例延迟记忆竞争神经网络的有限时间同步
本文考虑了比例延迟记忆竞争神经网络(PDMCNN)的有限时间同步(FTS)问题。通过利用 Lyapunov 函数方法和微分包容理论,建立了两个确保 PDMCNN 的有限时间同步的新准则。与矩阵不等式相比,这些具有代数不等式形式的准则不那么复杂,也更容易验证。此外,还估算了相应的沉降时间。最后,通过两个数值示例证实了所提出的准则和控制器的有效性,并提供了一个关于图像加密的应用。
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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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.
期刊最新文献
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