96,568 research outputs found
Uniform synchronous criticality of diversely random complex networks
We investigate collective synchronous behaviors in random complex networks of
limit-cycle oscillators with the non-identical asymmetric coupling scheme, and
find a uniform coupling criticality of collective synchronization which is
independent of complexity of network topologies. Numerically simulations on
categories of random complex networks have verified this conclusion.Comment: 8 pages, 4 figure
Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network
We introduce an easily computable topological measure which locates the
effective crossover between segregation and integration in a modular network.
Segregation corresponds to the degree of network modularity, while integration
is expressed in terms of the algebraic connectivity of an associated
hyper-graph. The rigorous treatment of the simplified case of cliques of equal
size that are gradually rewired until they become completely merged, allows us
to show that this topological crossover can be made to coincide with a
dynamical crossover from cluster to global synchronization of a system of
coupled phase oscillators. The dynamical crossover is signaled by a peak in the
product of the measures of intra-cluster and global synchronization, which we
propose as a dynamical measure of complexity. This quantity is much easier to
compute than the entropy (of the average frequencies of the oscillators), and
displays a behavior which closely mimics that of the dynamical complexity index
based on the latter. The proposed toplogical measure simultaneously provides
information on the dynamical behavior, sheds light on the interplay between
modularity vs total integration and shows how this affects the capability of
the network to perform both local and distributed dynamical tasks
Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks
Cooperative localization in agent networks based on interagent time-of-flight
measurements is closely related to synchronization. To leverage this relation,
we propose a Bayesian factor graph framework for cooperative simultaneous
localization and synchronization (CoSLAS). This framework is suited to mobile
agents and time-varying local clock parameters. Building on the CoSLAS factor
graph, we develop a distributed (decentralized) belief propagation algorithm
for CoSLAS in the practically important case of an affine clock model and
asymmetric time stamping. Our algorithm allows for real-time operation and is
suitable for a time-varying network connectivity. To achieve high accuracy at
reduced complexity and communication cost, the algorithm combines particle
implementations with parametric message representations and takes advantage of
a conditional independence property. Simulation results demonstrate the good
performance of the proposed algorithm in a challenging scenario with
time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE
Transaction on Signal Processin
Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
Networks of coupled dynamical systems provide a powerful way to model systems
with enormously complex dynamics, such as the human brain. Control of
synchronization in such networked systems has far reaching applications in many
domains, including engineering and medicine. In this paper, we formulate the
synchronization control in dynamical systems as an optimization problem and
present a multi-objective genetic programming-based approach to infer optimal
control functions that drive the system from a synchronized to a
non-synchronized state and vice-versa. The genetic programming-based controller
allows learning optimal control functions in an interpretable symbolic form.
The effectiveness of the proposed approach is demonstrated in controlling
synchronization in coupled oscillator systems linked in networks of increasing
order complexity, ranging from a simple coupled oscillator system to a
hierarchical network of coupled oscillators. The results show that the proposed
method can learn highly-effective and interpretable control functions for such
systems.Comment: Submitted to nonlinear dynamic
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