标题： 自适应网络中的循环混沌聚类和慢混沌 显示英文标题

标题： Recurrent chaotic clustering and slow chaos in adaptive networks

摘要： 自适应动力网络是结构与节点的动力学状态共同进化和相互作用的网络系统。 我们研究一种自适应动力网络，其中结构的变化相对于节点的快速动力学在更慢的时间尺度上发生。 我们识别出一种我们称为重复自适应混沌聚类（RACC）的现象，在这种现象中，混沌在慢时间尺度上被观察到，而快速时间尺度则表现出规则的动力学。 这种慢混沌进一步以长的（相对于快速时间尺度）频率簇或频率同步动力学阶段为特征，并被这些阶段之间的快速跳跃所中断。 我们还确定了跳跃之间时间间隔呈现混沌的参数值，并表明这种状态对参数和初始条件的变化具有鲁棒性。 显示英文摘要

摘要： Adaptive dynamical networks are network systems in which the structure co-evolves and interacts with the dynamical state of the nodes. We study an adaptive dynamical network in which the structure changes on a slower time scale relative to the fast dynamics of the nodes. We identify a phenomenon we refer to as recurrent adaptive chaotic clustering (RACC), in which chaos is observed on a slow time scale, while the fast time scale exhibits regular dynamics. Such slow chaos is further characterized by long (relative to the fast time scale) regimes of frequency clusters or frequency-synchronized dynamics, interrupted by fast jumps between these regimes. We also determine parameter values where the time intervals between jumps are chaotic and show that such a state is robust to changes in parameters and initial conditions.