Paper Info
Title
Adaptive identification of chaotic systems and its applications in chaotic communications
Abstract
A novel method for identifying a chaotic system with time-varying bifurcation parameters via an observation signal which has been contaminated by additive white Gaussian noise (AWGN) is developed. This method is based on an adaptive algorithm which takes advantage of the good approximation capability of the Radial Basis Function (RBF) neural network and the ability of the Extended Kalman Filter (EKF) for tracking a time-varying dynamical system. It is demonstrated that, provided the bifurcation parameter varies slowly in a time window, a chaotic dynamical system can be tracked and identified continuously, and the time-varying bifurcation parameter can also be retrieved in a sub-window of time via a simple least-square-fit method.
Year
DOI
Venue
2005
10.1007/11539117_49
ICNC (2)
Keywords
Field
DocType
extended kalman filter,novel method,chaotic system,time window,chaotic communication,time-varying bifurcation parameter,radial basis function,chaotic dynamical system,time-varying dynamical system,simple least-square-fit method,adaptive identification,bifurcation parameter,dynamic system,least square,additive white gaussian noise
Extended Kalman filter,Radial basis function network,Control theory,Computer science,White noise,Adaptive algorithm,System identification,Chaotic,Gaussian noise,Additive white Gaussian noise
Conference
Volume
ISSN
ISBN
3611
0302-9743
3-540-28325-0
Citations 
PageRank 
References 
0
0.34
2
Authors
1
Name
Order
Citations
PageRank
Jiuchao Feng113317.84