1,360 research outputs found
Projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks
We study projective-anticipating, projective, and projective-lag
synchronization of time-delayed chaotic systems on random networks. We relax
some limitations of previous work, where projective-anticipating and
projective-lag synchronization can be achieved only on two coupled chaotic
systems. In this paper, we can realize projective-anticipating and
projective-lag synchronization on complex dynamical networks composed by a
large number of interconnected components. At the same time, although previous
work studied projective synchronization on complex dynamical networks, the
dynamics of the nodes are coupled partially linear chaotic systems. In this
paper, the dynamics of the nodes of the complex networks are time-delayed
chaotic systems without the limitation of the partial-linearity. Based on the
Lyapunov stability theory, we suggest a generic method to achieve the
projective-anticipating, projective, and projective-lag synchronization of
time-delayed chaotic systems on random dynamical networks and find both the
existence and sufficient stability conditions. The validity of the proposed
method is demonstrated and verified by examining specific examples using Ikeda
and Mackey-Glass systems on Erdos-Renyi networks.Comment: 14 pages, 6 figure
Effects of degree distribution in mutual synchronization of neural networks
We study the effects of the degree distribution in mutual synchronization of
two-layer neural networks. We carry out three coupling strategies: large-large
coupling, random coupling, and small-small coupling. By computer simulations
and analytical methods, we find that couplings between nodes with large degree
play an important role in the synchronization. For large-large coupling, less
couplings are needed for inducing synchronization for both random and
scale-free networks. For random coupling, cutting couplings between nodes with
large degree is very efficient for preventing neural systems from
synchronization, especially when subnetworks are scale-free.Comment: 5 pages, 4 figure
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