非同层复用网络中的层内同步和层间准同步。

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 : 2023-11-06 DOI:10.1109/TNNLS.2023.3326629

Yujuan Han, Wenlian Lu, Tianping Chen

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引用次数: 0

摘要

在本文中，我们讨论了不同层的多路复用网络中的同步。不同层中的拓扑结构和非耦合节点动力学都是不同的。根据非耦合系统的耦合矩阵、耦合强度和本征函数，导出了层内同步和层间准同步的新的充分准则。我们还研究了具有相同解耦节点动力学的多路复用网络的层间同步。最后，通过算例验证了这些理论结果的有效性。

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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Intralayer Synchronization and Interlayer Quasisynchronization in Multiplex Networks of Nonidentical Layers.

In this article, we discuss synchronization in multiplex networks of different layers. Both the topologies and the uncoupled node dynamics in different layers are different. Novel sufficient criteria are derived for intralayer synchronization and interlayer quasisynchronization, in terms of the coupling matrices, the coupling strengths, and the intrinsic function of the uncoupled systems. We also investigate interlayer synchronization of multiplex networks with identical uncoupled node dynamics. Finally, we give some numerical examples to validate the effectiveness of these theoretical results.

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来源期刊

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.