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.
Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, Wisconsin 53792-7375, USA.
Chao
C
Tao
Yu
Y
Zhang
Jack J
JJ
Jiang
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