Nonparametric system identification by kernel methods

A new nonparametric estimate for nonlinear discrete-time dynamic systems is considered. The new algorithm is weakly consistent under a specific condition on the transition probability operator of a stationary Markov process. The estimate is applicable when a parametric state model of the system is difficult to choose.