16,277 research outputs found
Adaptive Exponential Synchronization of Coupled Complex Networks on General Graphs
We investigate the synchronization in complex dynamical networks, where the coupling configuration corresponds to a weighted graph. An adaptive synchronization method on general coupling configuration graphs is given. The networks may synchronize at an arbitrarily given exponential rate by enhancing the updated law of the variable coupling strength and achieve synchronization more quickly by adding edges to original graphs. Finally, numerical simulations are provided to illustrate the effectiveness of our theoretical results
Cluster synchronization in networks of coupled non-identical dynamical systems
In this paper, we study cluster synchronization in networks of coupled
non-identical dynamical systems. The vertices in the same cluster have the same
dynamics of uncoupled node system but the uncoupled node systems in different
clusters are different. We present conditions guaranteeing cluster
synchronization and investigate the relation between cluster synchronization
and the unweighted graph topology. We indicate that two condition play key
roles for cluster synchronization: the common inter-cluster coupling condition
and the intra-cluster communication. From the latter one, we interpret the two
well-known cluster synchronization schemes: self-organization and driving, by
whether the edges of communication paths lie at inter or intra-cluster. By this
way, we classify clusters according to whether the set of edges inter- or
intra-cluster edges are removable if wanting to keep the communication between
pairs of vertices in the same cluster. Also, we propose adaptive feedback
algorithms on the weights of the underlying graph, which can synchronize any
bi-directed networks satisfying the two conditions above. We also give several
numerical examples to illustrate the theoretical results
Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach
This work analyzes the problem of community structure in real-world networks
based on the synchronization of nonidentical coupled chaotic R\"{o}ssler
oscillators each one characterized by a defined natural frequency, and coupled
according to a predefined network topology. The interaction scheme contemplates
an uniformly increasing coupling force to simulate a society in which the
association between the agents grows in time. To enhance the stability of the
correlated states that could emerge from the synchronization process, we
propose a parameterless mechanism that adapts the characteristic frequencies of
coupled oscillators according to a dynamic connectivity matrix deduced from
correlated data. We show that the characteristic frequency vector that results
from the adaptation mechanism reveals the underlying community structure
present in the network.Comment: 21 pages, 7 figures; Chaos, Solitons & Fractals (2012
Dynamical Systems on Networks: A Tutorial
We give a tutorial for the study of dynamical systems on networks. We focus
especially on "simple" situations that are tractable analytically, because they
can be very insightful and provide useful springboards for the study of more
complicated scenarios. We briefly motivate why examining dynamical systems on
networks is interesting and important, and we then give several fascinating
examples and discuss some theoretical results. We also briefly discuss
dynamical systems on dynamical (i.e., time-dependent) networks, overview
software implementations, and give an outlook on the field.Comment: 39 pages, 1 figure, submitted, more examples and discussion than
original version, some reorganization and also more pointers to interesting
direction
Heterogeneity induces emergent functional networks for synchronization
We study the evolution of heterogeneous networks of oscillators subject to a
state-dependent interconnection rule. We find that heterogeneity in the node
dynamics is key in organizing the architecture of the functional emerging
networks. We demonstrate that increasing heterogeneity among the nodes in
state-dependent networks of phase oscillators causes a differentiation in the
activation probabilities of the links. This, in turn, yields the formation of
hubs associated to nodes with larger distances from the average frequency of
the ensemble. Our generic local evolutionary strategy can be used to solve a
wide range of synchronization and control problems
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
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