Title :
Nonparametric kernel algorithm for recovery of functions from noisy measurements with applications
Author :
Georgiev, Alexander A.
Author_Institution :
Technical University of Wroclaw, Wroclaw, Poland
fDate :
8/1/1985 12:00:00 AM
Abstract :
This note presents a kernel algorithm for recovery of a regression function from noisy data. Conditions are provided that assure pointwise convergence in the mean square and almost sure senses. An application to a class of linear system identification problems is discussed.
Keywords :
Nonparametric estimation; System identification, linear systems; Convergence; Cybernetics; Kernel; Least squares approximation; Linear systems; Maximum likelihood estimation; Memoryless systems; Random variables; Sufficient conditions; System identification;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1985.1104052
