5,917 research outputs found
Spectral control for ecological stability
A system made up of N interacting species is considered. Self-reaction terms
are assumed of the logistic type. Pairwise interactions take place among
species according to different modalities, thus yielding a complex asymmetric
disordered graph. A mathematical procedure is introduced and tested to
stabilise the ecosystem via an {\it ad hoc} rewiring of the underlying
couplings. The method implements minimal modifications to the spectrum of the
Jacobian matrix which sets the stability of the fixed point and traces these
changes back to species-species interactions. Resilience of the equilibrium
state appear to be favoured by predator-prey interactions
Global topological control for synchronized dynamics on networks
A general scheme is proposed and tested to control the symmetry breaking
instability of a homogeneous solution of a spatially extended multispecies
model, defined on a network. The inherent discreteness of the space makes it
possible to act on the topology of the inter-nodes contacts to achieve the
desired degree of stabilization, without altering the dynamical parameters of
the model. Both symmetric and asymmetric couplings are considered. In this
latter setting the web of contacts is assumed to be balanced, for the
homogeneous equilibrium to exist. The performance of the proposed method are
assessed, assuming the Complex Ginzburg-Landau equation as a reference model.
In this case, the implemented control allows one to stabilize the synchronous
limit cycle, hence time-dependent, uniform solution. A system of coupled real
Ginzburg-Landau equations is also investigated to obtain the topological
stabilization of a homogeneous and constant fixed point
Mathematical problems for complex networks
Copyright @ 2012 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available through the Brunel Open Access Publishing Fund.Complex networks do exist in our lives. The brain is a neural network. The global economy
is a network of national economies. Computer viruses routinely spread through the Internet. Food-webs, ecosystems, and metabolic pathways can be represented by networks. Energy is distributed through transportation networks in living organisms, man-made infrastructures, and other physical systems. Dynamic behaviors of complex networks, such as stability, periodic oscillation, bifurcation, or even chaos, are ubiquitous in the real world and often reconfigurable. Networks have been studied in the context of dynamical systems in a range of disciplines. However, until recently there has been relatively little work that treats dynamics as a function of network structure, where the states of both the nodes and the edges can change, and the topology of the network itself often evolves in time. Some major problems have not been fully investigated, such as the behavior of stability, synchronization and chaos control for complex networks, as well as their applications in, for example, communication and bioinformatics
Stabilization of Networked Control Systems with Sparse Observer-Controller Networks
In this paper we provide a set of stability conditions for linear
time-invariant networked control systems with arbitrary topology, using a
Lyapunov direct approach. We then use these stability conditions to provide a
novel low-complexity algorithm for the design of a sparse observer-based
control network. We employ distributed observers by employing the output of
other nodes to improve the stability of each observer dynamics. To avoid
unbounded growth of controller and observer gains, we impose bounds on their
norms. The effects of relaxation of these bounds is discussed when trying to
find the complete decentralization conditions
Coordinated optimization of visual cortical maps (II) Numerical studies
It is an attractive hypothesis that the spatial structure of visual cortical
architecture can be explained by the coordinated optimization of multiple
visual cortical maps representing orientation preference (OP), ocular dominance
(OD), spatial frequency, or direction preference. In part (I) of this study we
defined a class of analytically tractable coordinated optimization models and
solved representative examples in which a spatially complex organization of the
orientation preference map is induced by inter-map interactions. We found that
attractor solutions near symmetry breaking threshold predict a highly ordered
map layout and require a substantial OD bias for OP pinwheel stabilization.
Here we examine in numerical simulations whether such models exhibit
biologically more realistic spatially irregular solutions at a finite distance
from threshold and when transients towards attractor states are considered. We
also examine whether model behavior qualitatively changes when the spatial
periodicities of the two maps are detuned and when considering more than 2
feature dimensions. Our numerical results support the view that neither minimal
energy states nor intermediate transient states of our coordinated optimization
models successfully explain the spatially irregular architecture of the visual
cortex. We discuss several alternative scenarios and additional factors that
may improve the agreement between model solutions and biological observations.Comment: 55 pages, 11 figures. arXiv admin note: substantial text overlap with
arXiv:1102.335
- …