复杂网络定向最优控制的能量标度
Energy Scaling of Targeted Optimal Control of Complex Networks
AI总结：
本文提出通过控制部分节点状态而非全部节点来降低控制能量，发现目标节点数越多，所需能量呈指数衰减，为设计更高效的控制策略提供了理论依据。
摘要：
最近研究表明，仅通过少量控制输入控制复杂网络动态系统时所需控制能量极大。大多数减少控制能量的方法集中在确定额外控制输入的位置。相反，本文表明通过控制网络中部分节点的状态而非所有节点的状态，在保持控制信号数量不变的情况下，可以显著降低控制部分网络所需能量。所需能量随着目标节点数的增加呈指数衰减，表明只要目标集适当 sizing，大型网络可通过相对少量的输入控制。我们通过模型和真实网络验证结论，得出能量标度定律，以更好地设计控制目标，无论系统规模、能量限制、状态限制、输入节点选择和目标节点选择如何。
英文摘要：
Recently it has been shown that the control energy required to control a dynamical complex network is prohibitively large when there are only a few control inputs. Most methods to reduce the control energy have focused on where, in the network, to place additional control inputs. Here, in contrast, we show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, while holding the number of control signals constant, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. We validate our conclusions in model and real networks to arrive at an energy scaling law to better design control objectives regardless of system size, energy restrictions, state restrictions, input node choices and target node choices.
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。
