Detecting Generalized Synchronization Between Chaotic Signals: A Kernel-based Approach
Chaotic Dynamics
2009-11-11 v2 Statistical Mechanics
Data Analysis, Statistics and Probability
Abstract
A unified framework for analyzing generalized synchronization in coupled chaotic systems from data is proposed. The key of the proposed approach is the use of the kernel methods recently developed in the field of machine learning. Several successful applications are presented, which show the capability of the kernel-based approach for detecting generalized synchronization. It is also shown that the dynamical change of the coupling coefficient between two chaotic systems can be captured by the proposed approach.
Cite
@article{arxiv.nlin/0507006,
title = {Detecting Generalized Synchronization Between Chaotic Signals: A Kernel-based Approach},
author = {Hiromichi Suetani and Yukito Iba and Kazuyuki Aihara},
journal= {arXiv preprint arXiv:nlin/0507006},
year = {2009}
}
Comments
20 pages, 15 figures. massively revised as a full paper; issues on the choice of parameters by cross validation, tests by surrogated data, etc. are added as well as additional examples and figures