Inferring the connectivity of coupled chaotic oscillators using Kalman filtering

Abstract Inferring the interactions between coupled oscillators is a significant open problem in complexity science, with multiple interdisciplinary applications. While the Kalman filter (KF) technique is a well-known tool, widely used for data assimilation and parameter estimation, to the best of o...

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Autores principales:	E. Forero-Ortiz, G. Tirabassi, C. Masoller, A. J. Pons
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b0af63ef618243f58ae618b019107fc4
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spelling	oai:doaj.org-article:b0af63ef618243f58ae618b019107fc42021-11-21T12:23:17ZInferring the connectivity of coupled chaotic oscillators using Kalman filtering10.1038/s41598-021-01444-72045-2322https://doaj.org/article/b0af63ef618243f58ae618b019107fc42021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01444-7https://doaj.org/toc/2045-2322Abstract Inferring the interactions between coupled oscillators is a significant open problem in complexity science, with multiple interdisciplinary applications. While the Kalman filter (KF) technique is a well-known tool, widely used for data assimilation and parameter estimation, to the best of our knowledge, it has not yet been used for inferring the connectivity of coupled chaotic oscillators. Here we demonstrate that KF allows reconstructing the interaction topology and the coupling strength of a network of mutually coupled Rössler-like chaotic oscillators. We show that the connectivity can be inferred by considering only the observed dynamics of a single variable of the three that define the phase space of each oscillator. We also show that both the coupling strength and the network architecture can be inferred even when the oscillators are close to synchronization. Simulation results are provided to show the effectiveness and applicability of the proposed method.E. Forero-OrtizG. TirabassiC. MasollerA. J. PonsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q E. Forero-Ortiz G. Tirabassi C. Masoller A. J. Pons Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
description	Abstract Inferring the interactions between coupled oscillators is a significant open problem in complexity science, with multiple interdisciplinary applications. While the Kalman filter (KF) technique is a well-known tool, widely used for data assimilation and parameter estimation, to the best of our knowledge, it has not yet been used for inferring the connectivity of coupled chaotic oscillators. Here we demonstrate that KF allows reconstructing the interaction topology and the coupling strength of a network of mutually coupled Rössler-like chaotic oscillators. We show that the connectivity can be inferred by considering only the observed dynamics of a single variable of the three that define the phase space of each oscillator. We also show that both the coupling strength and the network architecture can be inferred even when the oscillators are close to synchronization. Simulation results are provided to show the effectiveness and applicability of the proposed method.
format	article
author	E. Forero-Ortiz G. Tirabassi C. Masoller A. J. Pons
author_facet	E. Forero-Ortiz G. Tirabassi C. Masoller A. J. Pons
author_sort	E. Forero-Ortiz
title	Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
title_short	Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
title_full	Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
title_fullStr	Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
title_full_unstemmed	Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
title_sort	inferring the connectivity of coupled chaotic oscillators using kalman filtering
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b0af63ef618243f58ae618b019107fc4
work_keys_str_mv	AT eforeroortiz inferringtheconnectivityofcoupledchaoticoscillatorsusingkalmanfiltering AT gtirabassi inferringtheconnectivityofcoupledchaoticoscillatorsusingkalmanfiltering AT cmasoller inferringtheconnectivityofcoupledchaoticoscillatorsusingkalmanfiltering AT ajpons inferringtheconnectivityofcoupledchaoticoscillatorsusingkalmanfiltering
_version_	1718419088230842368

Inferring the connectivity of coupled chaotic oscillators using Kalman filtering

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