DocumentCode
844844
Title
Nonparametric system identification by kernel methods
Author
Georgiev, Alexander A.
Author_Institution
Technical University of Wroclaw, Wroclaw, Poland
Volume
29
Issue
4
fYear
1984
fDate
4/1/1984 12:00:00 AM
Firstpage
356
Lastpage
358
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;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.1984.1103532
Filename
1103532
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