Convergence Rate of the Causal Jacobi ...
Type de document :
Partie d'ouvrage: Chapitre
Titre :
Convergence Rate of the Causal Jacobi Derivative Estimator
Auteur(s) :
Liu, Da-Yan [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Non-Asymptotic estimation for online systems [NON-A]
Gibaru, Olivier [Auteur]
Laboratoire de Métrologie et de Mathématiques Appliquées [L2MA]
Non-Asymptotic estimation for online systems [NON-A]
Perruquetti, Wilfrid [Auteur]
Centrale Lille
Systèmes Non Linéaires et à Retards [SyNeR]
Non-Asymptotic estimation for online systems [NON-A]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Non-Asymptotic estimation for online systems [NON-A]
Gibaru, Olivier [Auteur]
Laboratoire de Métrologie et de Mathématiques Appliquées [L2MA]
Non-Asymptotic estimation for online systems [NON-A]
Perruquetti, Wilfrid [Auteur]

Centrale Lille
Systèmes Non Linéaires et à Retards [SyNeR]
Non-Asymptotic estimation for online systems [NON-A]
Titre de l’ouvrage :
Curves and Surfaces 2011
Date de publication :
2011
Discipline(s) HAL :
Mathématiques [math]/Analyse numérique [math.NA]
Résumé en anglais : [en]
Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new paradigms in solid modeling and video ...
Lire la suite >Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new paradigms in solid modeling and video compression. By using an analytical point of view due to Lanczos \cite{C. Lanczos} to this causal case, we revisit $n^{th}$\ order derivative estimators originally introduced within an algebraic framework by Mboup, Fliess and Join in \cite{num,num0}. Thanks to a given noise level $\delta$ and a well-suitable integration length window, we show that the derivative estimator error can be $\mathcal{O}(\delta ^{\frac{q+1}{n+1+q}})$ where $q$\ is the order of truncation of the Jacobi polynomial series expansion used. This so obtained bound helps us to choose the values of our parameter estimators. We show the efficiency of our method on some examples.Lire moins >
Lire la suite >Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new paradigms in solid modeling and video compression. By using an analytical point of view due to Lanczos \cite{C. Lanczos} to this causal case, we revisit $n^{th}$\ order derivative estimators originally introduced within an algebraic framework by Mboup, Fliess and Join in \cite{num,num0}. Thanks to a given noise level $\delta$ and a well-suitable integration length window, we show that the derivative estimator error can be $\mathcal{O}(\delta ^{\frac{q+1}{n+1+q}})$ where $q$\ is the order of truncation of the Jacobi polynomial series expansion used. This so obtained bound helps us to choose the values of our parameter estimators. We show the efficiency of our method on some examples.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.inria.fr/inria-00599767/document
- Accès libre
- Accéder au document
- http://arxiv.org/pdf/1106.2208
- Accès libre
- Accéder au document
- https://hal.inria.fr/inria-00599767/document
- Accès libre
- Accéder au document
- https://hal.inria.fr/inria-00599767/document
- Accès libre
- Accéder au document
- https://hal.inria.fr/inria-00599767/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- liu.pdf
- Accès libre
- Accéder au document
- 1106.2208
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- liu.pdf
- Accès libre
- Accéder au document