DocumentCode
336179
Title
Nonlinear filtering by kriging, with application to system inversion
Author
Costa, J.-P. ; Pronzato, L. ; Thierry, E.
Author_Institution
CNRS-UNSA, Biot, France
Volume
3
fYear
1999
fDate
15-19 Mar 1999
Firstpage
1313
Abstract
Prediction by kriging does not rely on any specific model structure, and is thus much more flexible than approaches based on parametric behavioural models. Since accurate predictions are obtained for extremely short training sequences, it generally performs better than prediction methods using parametric models. Application to nonlinear system inversion is considered
Keywords
Gaussian processes; covariance matrices; filtering theory; inverse problems; maximum likelihood estimation; nonlinear filters; prediction theory; random processes; sequences; statistical analysis; Gaussian process; MLE; SISO nonlinear system; covariance matrix; kriging prediction; linear regression; nonlinear filtering; nonlinear system inversion; parametric models; random process; short training sequences; Autoregressive processes; Filtering; Linear regression; Nonlinear systems; Parametric statistics; Polynomials; Prediction methods; Predictive models; Random processes; Signal processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location
Phoenix, AZ
ISSN
1520-6149
Print_ISBN
0-7803-5041-3
Type
conf
DOI
10.1109/ICASSP.1999.756221
Filename
756221
Link To Document