58,443 research outputs found
A new self-organizing neural gas model based on Bregman divergences
In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural
Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization
capabilities.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tec
Distributed coordination of self-organizing mechanisms in communication networks
The fast development of the Self-Organizing Network (SON) technology in
mobile networks renders the problem of coordinating SON functionalities
operating simultaneously critical. SON functionalities can be viewed as control
loops that may need to be coordinated to guarantee conflict free operation, to
enforce stability of the network and to achieve performance gain. This paper
proposes a distributed solution for coordinating SON functionalities. It uses
Rosen's concave games framework in conjunction with convex optimization. The
SON functionalities are modeled as linear Ordinary Differential Equation
(ODE)s. The stability of the system is first evaluated using a basic control
theory approach. The coordination solution consists in finding a linear map
(called coordination matrix) that stabilizes the system of SON functionalities.
It is proven that the solution remains valid in a noisy environment using
Stochastic Approximation. A practical example involving three different SON
functionalities deployed in Base Stations (BSs) of a Long Term Evolution (LTE)
network demonstrates the usefulness of the proposed method.Comment: submitted to IEEE TCNS. arXiv admin note: substantial text overlap
with arXiv:1209.123
Magnification Control in Self-Organizing Maps and Neural Gas
We consider different ways to control the magnification in self-organizing
maps (SOM) and neural gas (NG). Starting from early approaches of magnification
control in vector quantization, we then concentrate on different approaches for
SOM and NG. We show that three structurally similar approaches can be applied
to both algorithms: localized learning, concave-convex learning, and winner
relaxing learning. Thereby, the approach of concave-convex learning in SOM is
extended to a more general description, whereas the concave-convex learning for
NG is new. In general, the control mechanisms generate only slightly different
behavior comparing both neural algorithms. However, we emphasize that the NG
results are valid for any data dimension, whereas in the SOM case the results
hold only for the one-dimensional case.Comment: 24 pages, 4 figure
Capturing pattern bi-stability dynamics in delay-coupled swarms
Swarms of large numbers of agents appear in many biological and engineering
fields. Dynamic bi-stability of co-existing spatio-temporal patterns has been
observed in many models of large population swarms. However, many reduced
models for analysis, such as mean-field (MF), do not capture the bifurcation
structure of bi-stable behavior. Here, we develop a new model for the dynamics
of a large population swarm with delayed coupling. The additional physics
predicts how individual particle dynamics affects the motion of the entire
swarm. Specifically, (1) we correct the center of mass propulsion physics
accounting for the particles velocity distribution; (2) we show that the model
we develop is able to capture the pattern bi-stability displayed by the full
swarm model.Comment: 6 pages 4 figure
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