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

2 Scopus citations


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
Number of pages13
StatePublished - 2010

Publication series

NameSpringer Optimization and Its Applications
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

ASJC Scopus subject areas

  • Control and Optimization


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