Title :
Nonparametric system identification by kernel methods
Author :
Georgiev, Alexander A.
Author_Institution :
Technical University of Wroclaw, Wroclaw, Poland
fDate :
4/1/1984 12:00:00 AM
Abstract :
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.
Keywords :
Nonparametric estimation; System identification, nonlinear systems; Control engineering; Control theory; Convergence; Kernel; Markov processes; Nonlinear dynamical systems; Predictive models; Probability density function; State estimation; System identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1984.1103532
