Deterministic learning of nonlinear dynamical systems
In this paper, we investigate the problem of identifying or modeling nonlinear dynamical systems undergoing periodic and period-like (recurrent) motions. For accurate identification of nonlinear dynamical systems, the persistent excitation condition is normally required to be satisfied. Firstly, by using localized radial basis function networks, a relationship between the recurrent trajectories and the persistence of excitation condition is established. Secondly, for a broad class of recurrent...[Show more]
|Collections||ANU Research Publications|
|Source:||International Journal of Bifurcation and Chaos|
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