A robust estimation of information flow in coupled nonlinear systems

Shivkumar Sabesan, Konstantinos Tsakalis, Andreas Spanias, Leon Iasemidis

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. Second, a new measure, the net transfer of entropy (NTE), is defined based on TE. Third, we employ surrogate analysis to show the statistical significance of the measures. Fourth, the effect of measurement noise on the measures’ performance is investigated up to S/N = 3 dB. We demonstrate the usefulness of the improved method by analyzing data series from coupled nonlinear chaotic oscillators. Our findings suggest that TE and NTE may play a critical role in elucidating the functional connectivity of complex networks of nonlinear systems.

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Pages271-283
Number of pages13
Volume38
DOIs
StatePublished - 2010

Publication series

NameSpringer Optimization and Its Applications
Volume38
ISSN (Print)19316828
ISSN (Electronic)19316836

Fingerprint

Robust Estimation
Information Flow
Coupled System
Nonlinear Systems
Entropy
Chaotic Oscillator
Series
Nonlinear Oscillator
Statistical Significance
Complex Networks
Performance Measures
Complex Systems
Connectivity
Linear Systems
Robustness
Demonstrate

ASJC Scopus subject areas

  • Control and Optimization

Cite this

Sabesan, S., Tsakalis, K., Spanias, A., & Iasemidis, L. (2010). A robust estimation of information flow in coupled nonlinear systems. In Springer Optimization and Its Applications (Vol. 38, pp. 271-283). (Springer Optimization and Its Applications; Vol. 38). Springer International Publishing. https://doi.org/10.1007/978-0-387-88630-5_15

A robust estimation of information flow in coupled nonlinear systems. / Sabesan, Shivkumar; Tsakalis, Konstantinos; Spanias, Andreas; Iasemidis, Leon.

Springer Optimization and Its Applications. Vol. 38 Springer International Publishing, 2010. p. 271-283 (Springer Optimization and Its Applications; Vol. 38).

Research output: Chapter in Book/Report/Conference proceedingChapter

Sabesan, S, Tsakalis, K, Spanias, A & Iasemidis, L 2010, A robust estimation of information flow in coupled nonlinear systems. in Springer Optimization and Its Applications. vol. 38, Springer Optimization and Its Applications, vol. 38, Springer International Publishing, pp. 271-283. https://doi.org/10.1007/978-0-387-88630-5_15
Sabesan S, Tsakalis K, Spanias A, Iasemidis L. A robust estimation of information flow in coupled nonlinear systems. In Springer Optimization and Its Applications. Vol. 38. Springer International Publishing. 2010. p. 271-283. (Springer Optimization and Its Applications). https://doi.org/10.1007/978-0-387-88630-5_15
Sabesan, Shivkumar ; Tsakalis, Konstantinos ; Spanias, Andreas ; Iasemidis, Leon. / A robust estimation of information flow in coupled nonlinear systems. Springer Optimization and Its Applications. Vol. 38 Springer International Publishing, 2010. pp. 271-283 (Springer Optimization and Its Applications).
@inbook{d31b70ddd0c94f1889d79e6cf2fadfd3,
title = "A robust estimation of information flow in coupled nonlinear systems",
abstract = "Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. Second, a new measure, the net transfer of entropy (NTE), is defined based on TE. Third, we employ surrogate analysis to show the statistical significance of the measures. Fourth, the effect of measurement noise on the measures’ performance is investigated up to S/N = 3 dB. We demonstrate the usefulness of the improved method by analyzing data series from coupled nonlinear chaotic oscillators. Our findings suggest that TE and NTE may play a critical role in elucidating the functional connectivity of complex networks of nonlinear systems.",
author = "Shivkumar Sabesan and Konstantinos Tsakalis and Andreas Spanias and Leon Iasemidis",
year = "2010",
doi = "10.1007/978-0-387-88630-5_15",
language = "English (US)",
volume = "38",
series = "Springer Optimization and Its Applications",
publisher = "Springer International Publishing",
pages = "271--283",
booktitle = "Springer Optimization and Its Applications",

}

TY - CHAP

T1 - A robust estimation of information flow in coupled nonlinear systems

AU - Sabesan, Shivkumar

AU - Tsakalis, Konstantinos

AU - Spanias, Andreas

AU - Iasemidis, Leon

PY - 2010

Y1 - 2010

N2 - Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. Second, a new measure, the net transfer of entropy (NTE), is defined based on TE. Third, we employ surrogate analysis to show the statistical significance of the measures. Fourth, the effect of measurement noise on the measures’ performance is investigated up to S/N = 3 dB. We demonstrate the usefulness of the improved method by analyzing data series from coupled nonlinear chaotic oscillators. Our findings suggest that TE and NTE may play a critical role in elucidating the functional connectivity of complex networks of nonlinear systems.

AB - Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. Second, a new measure, the net transfer of entropy (NTE), is defined based on TE. Third, we employ surrogate analysis to show the statistical significance of the measures. Fourth, the effect of measurement noise on the measures’ performance is investigated up to S/N = 3 dB. We demonstrate the usefulness of the improved method by analyzing data series from coupled nonlinear chaotic oscillators. Our findings suggest that TE and NTE may play a critical role in elucidating the functional connectivity of complex networks of nonlinear systems.

UR - http://www.scopus.com/inward/record.url?scp=84976517790&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84976517790&partnerID=8YFLogxK

U2 - 10.1007/978-0-387-88630-5_15

DO - 10.1007/978-0-387-88630-5_15

M3 - Chapter

AN - SCOPUS:84976517790

VL - 38

T3 - Springer Optimization and Its Applications

SP - 271

EP - 283

BT - Springer Optimization and Its Applications

PB - Springer International Publishing

ER -