eBangor

Quantifying sudden changes in dynamical systems using symbolic networks

Masoller, C. and Hong, Y. and Ayad, S. and Gustave, F. and Barland, S. and Pons, A.J. and Gomez, S. and Arenas, A. (2015) Quantifying sudden changes in dynamical systems using symbolic networks. New Journal of Physics, 17. DOI: 10.1088/1367-2630/17/2/023068

[img] Text
32178.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB)

Abstract

We characterize the evolution of a dynamical system by combining two well-known complex systems' tools, namely, symbolic ordinal analysis and networks. From the ordinal representation of a time series we construct a network in which every node weight represents the probability of an ordinal pattern (OP) to appear in the symbolic sequence and each edge's weight represents the probability of transitions between two consecutive OPs. Several network-based diagnostics are then proposed to characterize the dynamics of different systems: logistic, tent, and circle maps. We show that these diagnostics are able to capture changes produced in the dynamics as a control parameter is varied. We also apply our new measures to empirical data from semiconductor lasers and show that they are able to anticipate the polarization switchings, thus providing early warning signals of abrupt transitions.

Item Type: Article
Subjects: Research Publications
Departments: College of Physical and Applied Sciences > School of Electronic Engineering
Date Deposited: 30 May 2015 02:36
Last Modified: 23 Sep 2015 02:49
ISSN: 1367-2630
URI: http://e.bangor.ac.uk/id/eprint/4615
Identification Number: DOI: 10.1088/1367-2630/17/2/023068
Publisher: IOP Publishing
Administer Item Administer Item

eBangor is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.