Estimating system parameters from chaotic time series with synchronization optimized by a genetic algorithm.
A method is proposed to estimate system parameters by optimizing synchronization with a genetic algorithm. This method can effectively find the parameter values of a chaotic system with a rugged parameter landscape. Furthermore, even the parameters of a 200-dimensional coupled-map-lattice spatiotemporal chaotic system can be extracted from a scalar time series. Finally, a Chua's circuit experiment shows the capacity of this method to estimate multiple parameters of real systems.
Tao
Chao
C
Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin 53792-7375, USA.
Zhang
Yu
Y
Jiang
Jack J
JJ
D000465:Algorithms; D000818:Animals; D002627:Chemistry, Physical / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D013598:Systems Theory; D013997:Time Factors