正复复数网络的群体行为
Herding Positive, Complex Networks
AI总结：
本文研究了正系统在复网络上的群体可控性，探讨了如何通过添加输入节点或选择最佳单节点来确保系统群体可控性。
摘要：
复杂网络的控制问题在生物学和群体机器人等领域具有重要价值。然而，可控性条件过于严格，无法捕捉到一系列期望的行为。最近引入了群体可控性作为替代网络控制概念。本文假设正系统在复网络上演化，研究了某一类网络系统的群体可控性，并探讨了两个与确保系统群体可控性相关的问题。第一个问题是输入添加问题，研究哪些节点应接收输入以确保系统群体可控。第二个问题是选择最佳单节点以群体控制网络，当单个节点保证系统群体可控时。为选择最佳群体控制节点，引入了一种新的基于控制能量的群体可控性中心性度量。
英文摘要：
The problem of controlling complex networks is of interest to disciplines ranging from biology to swarm robotics. However, controllability can be too strict a condition, failing to capture a range of desirable behaviors. Herdability, which describes the ability to drive a system to a specific set in the state space, was recently introduced as an alternative network control notion. This paper considers the application of herdability to the study of complex networks under the assumption that a positive system evolves on the network. The herdability of a class of networked systems is investigated and two problems related to ensuring system herdability are explored. The first is the input addition problem, which investigates which nodes in a network should receive inputs to ensure that the system is herdable. The second is a related problem of selecting the best single node from which to herd the network, in the case that a single node is guaranteed to make the system is herdable. In order to select the best herding node, a novel control energy based herdability centrality measure is introduced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
