Recurrent chaotic clustering and slow chaos in adaptive networks
arxiv(2024)
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.
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