笛卡尔产品网络的离散时间可控性

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-06 DOI:10.1109/TSIPN.2024.3487411

Bo Liu;Mengjie Hu;Junjie Huang;Qiang Zhang;Yin Chen;Housheng Su

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引用次数: 0

摘要

本研究探讨了因子网络通过笛卡尔积形成的复合网络的离散时间可控性。基于 Popov-Belevitch-Hautus 检验和笛卡尔积的性质，我们推导了笛卡尔积网络（CPN）可控性的代数理论必要条件和充分条件，致力于全面研究节点系统动力学、网络拓扑结构与 CPN 可控性之间错综复杂的相互作用，特别是 CPN 与其因子之间的内在联系。这有助于我们丰富和完善复杂网络可控性的理论框架，并为设计更大规模复合网络的有效控制方案提供新的启示。

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Discrete-Time Controllability of Cartesian Product Networks

This work studies the discrete-time controllability of a composite network formed by factor networks via Cartesian products. Based on the Popov-Belevitch-Hautus test and properties of Cartesian products, we derive the algebra-theoretic necessary and sufficient conditions for the controllability of the Cartesian product network (CPN), which is devoted to carry out a comprehensive study of the intricate interplay between the node-system dynamics, network topology and the controllability of the CPN, especially the intrinsic connection between the CPN and its factors. This helps us enrich and perfect the theoretical framework of controllability of complex networks, and gives new insight into designing a valid control scheme for larger-scale composite networks.

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.