Estimation of all model parameters of chaotic systems from discrete scalar time series measurements

A strategy for estimating all parameters of chaotic systems using discretely sampled data of a measured scalar output is developed and tested. The effect of measurement noise on the asymptotic synchronization error is studied. The robustness of the method in the presence of noise and its ability to track changes in operating parameters of an experimental system is also demonstrated.