电晕产品网络的能量相关可控性

IF 6.8 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qiang Zhang , Junjie Huang , Bo Liu , Housheng Su , Alatancang Chen
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引用次数: 0

摘要

本文通过电晕积研究了一类由具有拉普拉斯动力学的“小”简单因子网络生成的“大”复合网络的能量相关可控性。与大多数现有的关于网络可控性的文献不同,本文从能量的角度刻画了电晕产物网络(CPNs)的可控性。这可以量化控制cpn的难度,基于可控性Gramian度量,包括平均可控性和体积控制能量等,其中能量是由领导者触发的。CPN的能量相关可控性可由其因子网络的特征值和特征向量来探讨。给出了一种求解最大平均可控性的算法,该算法可以帮助选择优化网络控制的领导者,并在实际中得到了应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy-related controllability of corona product networks
This article studies the energy-related controllability for a category of ‘large’ composite networks generated by ‘small’ simple factor networks with Laplacian dynamics under a leader-follower framework via corona product. Different from most existing literature on network controllability, this work characterizes the controllability of corona product networks (CPNs) from an energy point of view. This can quantify the difficulty of controlling CPNs based on controllability Gramian measures, involving average controllability and volumetric control energy, etc., where the energy is triggered by the leaders. The energy-related controllability of a CPN can be explored from the eigenvalues and eigenvectors of its factor networks. An algorithm for solving the maximum average controllability is provided, which can help one select the leaders to optimize network control and be applied in practice.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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