Nonlinear filtering by kriging, with application to system inversion

Nonlinear filtering by kriging, with application to system inversion

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 param...

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Bibliographic Details
Published in:1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) Vol. 3; pp. 1313 - 1316 vol.3
Main Authors: Costa, J.-P., Pronzato, L., Thierry, E.
Format: Conference Proceeding
Language:English
Published: IEEE 1999
Subjects:
Autoregressive processes
Filtering
Linear regression
Nonlinear systems
Parametric statistics
Polynomials
Prediction methods
Predictive models
Random processes
Signal processing
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Summary: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.
ISBN:0780350413
9780780350410
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1999.756221