Synchronization of reconstructed dynamical systems.
The problem of constructing synchronizing systems to observed signals is approached from a data driven perspective, in which it is assumed that neither the drive nor the response systems are known explicitly but have to be derived from the observations. The response systems are modeled by utilizing standard methods of nonlinear time series analysis applied to sections of the driving signals. As a result, synchronization is more robust than what might be expected, given that the reconstructed systems are only approximations of the unknown true systems. Successful synchronization also may be accomplished in cases where the driving signals result from nonlinearly transformed chaotic states. The method is readily extended and applied to limited real-time predictions of chaotic signals.
Voss
H U
HU
Freiburg Center for Data Analysis and Modeling (FDM), Eckerstr. 1, 79104 Freiburg, Germany.
D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D013223:Statistics as Topic