• Corpus ID: 22531069

Noise-induced synchronization of self-organized systems: Hegselmann-Krause dynamics in infinite space

@article{Su2017NoiseinducedSO,
title={Noise-induced synchronization of self-organized systems: Hegselmann-Krause dynamics in infinite space},
author={Wei Su and Jin Guo and Xianzhong Chen and Ge Chen},
journal={ArXiv},
year={2017},
volume={abs/1711.01432}
}
• Wei Su
• Published 4 November 2017
• Mathematics
• ArXiv
It has been well established the theoretical analysis for the noise-induced consensus of the local-rule based Hegselmann-Krause (HK) dynamics in finite space. However, when system states are allowed in the infinite space, severe mathematical difficulties arise, and the problem remains open. In this paper, we completely resolved the case when system states are allowed in the infinite space, and also the critical noise strength is given.

References

SHOWING 1-10 OF 22 REFERENCES

Small noise may diversify collective motion

• Ge Chen
• Engineering
2015 34th Chinese Control Conference (CCC)
• 2015
A general method is proposed which transfers the analysis of these systems to the design of cooperative control algorithms and rigorously analyzes the origin Vicsek model under both open and periodic boundary conditions for the first time, and makes some extensions to the inhomogenous SPP systems including the leader-follower models.

Noise-induced order

• Physics
• 1983
A new noise effect on chaos in one-dimensional mappings is reported. The transition from chaotic behavior to ordered behavior induced by external noise is observed in a certain class of

The Smallest Possible Interaction Radius for Synchronization of Self-Propelled Particles

• Mathematics
SIAM Rev.
• 2014
This paper will investigate a typical collective behavior of a self-propelled particle system modeled by the nearest neighbor rules and show that the smallest possible interaction radius approximately equals $\sqrt{\log n/(\pi n)}$, with $n$ being the population size, w...

Convergence of a Class of Multi-Agent Systems In Probabilistic Framework

• Mathematics, Computer Science
2007 46th IEEE Conference on Decision and Control
• 2007
This paper provides a complete and rigorous proof for the fact that the overall multi-agent system will synchronize with large probability as long as the number of agents is large enough.

Novel type of phase transition in a system of self-driven particles.

• Materials Science
Physical review letters
• 1995
Numerical evidence is presented that this model results in a kinetic phase transition from no transport to finite net transport through spontaneous symmetry breaking of the rotational symmetry.

Molecular communication through stochastic synchronization induced by extracellular fluctuations.

• Biology
Physical review letters
• 2005
A biologically plausible model is developed for cellular communication in an indirectly coupled multicellular system and extracellular noises that are common to all cells are investigated, which can induce collective dynamics and stochastically synchronize the multicesllular system.

Noise to order

• Physics
Nature
• 2001
Some of the underlying mechanisms believed to be at the heart of crystal growth, honeycomb manufacture and floret evolution generate regular and predictable patterns are discussed.

Coordination of groups of mobile autonomous agents using nearest neighbor rules

• Computer Science
Proceedings of the 41st IEEE Conference on Decision and Control, 2002.
• 2002
Simulation results are provided which demonstrate that the nearest neighbor rule they are studying can cause all agents to eventually move in the same direction despite the absence of centralized coordination and despite the fact that each agent's set of nearest neighbors change with time as the system evolves.

Noise in biology

• L. Tsimring
• Biology
Reports on progress in physics. Physical Society
• 2014
This short review covers the recent progress in understanding mechanisms and effects of fluctuations in biological systems of different scales and the basic approaches to their mathematical modeling.