Information Technology Reference
In-Depth Information
Chapter 15
A Robust Estimation of Information Flow
in Coupled Nonlinear Systems
Shivkumar Sabesan, Konstantinos Tsakalis, Andreas Spanias, and Leon Iasemidis
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 im-
provements 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 sys-
tems. 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
/
=
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 func-
tional connectivity of complex networks of nonlinear systems.
N
15.1 Introduction
Recent advances in information theory and nonlinear dynamics have facilitated
novel approaches for the study of the functional interactions between coupled
