DocumentCode :
1126608
Title :
Effective detection of coupling in short and noisy bivariate data
Author :
Bhattacharya, Joydeep ; Pereda, Ernesto ; Petsche, Hellmuth
Volume :
33
Issue :
1
fYear :
2003
fDate :
2/1/2003 12:00:00 AM
Firstpage :
85
Lastpage :
95
Abstract :
In the study of complex systems, one of the primary concerns is the characterization and quantification of interdependencies between different subsystems. In real-life systems, the nature of dependencies or coupling can be nonlinear and asymmetric, rendering the classical linear methods unsuitable for this purpose. Furthermore, experimental signals are noisy and short, which pose additional constraints for the measurement of underlying coupling. We discuss an index based on nonlinear dynamical system theory to measure the degree of coupling which can be asymmetric. The usefulness of this index has been demonstrated by several examples including simulated and real-life signals. This index is found to effectively disclose the nature and the degree of interactions even when the coupling is very weak and data are noisy and of limited length; by this way, new insight into the functioning of the underlying complex system is possible.
Keywords :
large-scale systems; noise; nonlinear dynamical systems; signal processing; time series; complex systems; coupling; index; interactions; nonlinear dynamical system theory; short noisy bivariate data; signals; subsystem interdependencies; Biology; Control systems; Evolution (biology); Mutual coupling; Mutual information; Noise measurement; Noise robustness; Nonlinear dynamical systems; Pollution measurement; State-space methods;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2003.808175
Filename :
1167356
Link To Document :
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