Synchronization and Control for Directed Multilevel Complex Networks With Arbitrary Couplings

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2025-05-27 DOI:10.1002/rnc.8065

Leijing Xie, Xiwei Liu

{"title":"Synchronization and Control for Directed Multilevel Complex Networks With Arbitrary Couplings","authors":"Leijing Xie, Xiwei Liu","doi":"10.1002/rnc.8065","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In this article, we focus on the synchronization problem of directed multilevel complex networks (MLevelN) with arbitrary couplings. First, we propose a new MLevelN model with arbitrarily connected couplings, thus previous fully or partially one-to-one connected couplings can be regarded as special cases for this model. Then, using the normalized left eigenvector of couplings, criteria on two typical types of synchronization for MLevelN are obtained. Besides, we consider MLevelN with disconnected layers, and by using Tarjan's algorithm, it can be rearranged to MLevelN with different number of nodes in different layers. Furthermore, different control strategies for the rearranged MLevelN are also designed, and pinning only one node (arbitrarily chosen) can make the whole MLevelN realize complete synchronization. Then, the results are applied on a three-level reaction-diffusion neural network as an application. Finally, the effectiveness of the obtained criteria is verified by numerical simulations.</p>\n </div>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"35 15","pages":"6338-6358"},"PeriodicalIF":3.2000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.8065","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

In this article, we focus on the synchronization problem of directed multilevel complex networks (MLevelN) with arbitrary couplings. First, we propose a new MLevelN model with arbitrarily connected couplings, thus previous fully or partially one-to-one connected couplings can be regarded as special cases for this model. Then, using the normalized left eigenvector of couplings, criteria on two typical types of synchronization for MLevelN are obtained. Besides, we consider MLevelN with disconnected layers, and by using Tarjan's algorithm, it can be rearranged to MLevelN with different number of nodes in different layers. Furthermore, different control strategies for the rearranged MLevelN are also designed, and pinning only one node (arbitrarily chosen) can make the whole MLevelN realize complete synchronization. Then, the results are applied on a three-level reaction-diffusion neural network as an application. Finally, the effectiveness of the obtained criteria is verified by numerical simulations.

查看原文本刊更多论文

具有任意耦合的有向多层复杂网络的同步与控制

本文主要研究具有任意耦合的有向多层复杂网络的同步问题。首先，我们提出了一个新的具有任意连接耦合的MLevelN模型，因此以前的完全或部分一对一连接耦合可以视为该模型的特殊情况。然后，利用耦合的归一化左特征向量，得到了两种典型MLevelN同步类型的判据。此外，我们考虑了多层不相连的MLevelN，通过Tarjan算法，可以将其重新排列为不同层中不同节点数的MLevelN。此外，对重新排列的MLevelN设计了不同的控制策略，仅固定一个节点（任意选择）即可使整个MLevelN实现完全同步。然后，将结果应用于三层反应扩散神经网络作为应用。最后，通过数值仿真验证了准则的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.