Please use this identifier to cite or link to this item: https://doi.org/10.1063/1.2171811
Title: Chaotic synchronization: A nonlinear predictive filtering approach
Authors: Kurian, A.P.
Puthusserypady, S. 
Issue Date: 2006
Source: Kurian, A.P., Puthusserypady, S. (2006). Chaotic synchronization: A nonlinear predictive filtering approach. Chaos 16 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1063/1.2171811
Abstract: The synchronization of chaotic systems is a difficult task due to their sensitive dependence on the initial conditions. Perfect synchronization is almost impossible when noise is present in the system. One of the well known stochastic filtering algorithms that is used to synchronize chaotic systems in the presence of noise is the extended Kalman filter (EKF). However, for highly nonlinear systems, the approximation error introduced by the EKF has been shown to be relatively high. In this paper, a nonlinear predictive filter (NPF) is proposed for synchronizing chaotic systems. In this scheme, it is not required to approximate the underlying nonlinearity and hence there is no need to compute the Jacobian of the chaotic system. Numerical simulations are carried out to compare the performances of the NPF and EKF algorithms for synchronizing different sets of chaotic systems and/or maps. The well known Lorenz and Mackey-Glass systems as well as Ikeda map are used for numerical evaluation of the performance. Results clearly show that the NPF based approach is superior to the EKF based approach in terms of the normalized mean square error (NMSE), total NMSE, and the time taken for synchronization (measured in terms of the normalized instantaneous square error) for all the systems and/or maps considered. © 2006 American Institute of Physics.
Source Title: Chaos
URI: http://scholarbank.nus.edu.sg/handle/10635/55264
ISSN: 10541500
DOI: 10.1063/1.2171811
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