title: Learning phase synchronization from nonsynchronized chaotic regimes.
abstract: We present a novel modeling approach for reconstruction of the global behavior of coupled chaotic systems from bivariate time series. We analyze two coupled chaotic oscillators, which are able to phase synchronize due to coupling. It is shown that our technique enables the recovery of the synchronization diagram from only three data sets. In particular, this allows the estimate of the relative strength of the coupling and the parameter mismatch of both subsystems. The method is most efficient if only data from the nonsynchronized regime are used for the model learning. We also apply this approach to experimental data of a paced plasma tube.
authors.affiliation: Department of Computer Science and Systems Engineering, Muroran Institute of Technology, Muroran, Hokkaido 050-8585, Japan.
authors.forename: Isao
authors.initials: I
authors.lastname: Tokuda
authors.forename: Jürgen
authors.initials: J
authors.lastname: Kurths
authors.forename: Epaminondas
authors.initials: E
authors.lastname: Rosa