A continuous-time framework for least ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
A continuous-time framework for least squares parameter estimation
Author(s) :
Riachy, Samer [Auteur correspondant]
Laboratoire QUARTZ [QUARTZ ]
Non-Asymptotic estimation for online systems [NON-A]
Laboratoire QUARTZ [QUARTZ ]
Non-Asymptotic estimation for online systems [NON-A]
Journal title :
Automatica
Pages :
6
Publisher :
Elsevier
Publication date :
2014-10-27
ISSN :
0005-1098
English keyword(s) :
least squares
parameter estimation
estimation under noise
Sobolev spaces
Evolution equations
parameter estimation
estimation under noise
Sobolev spaces
Evolution equations
HAL domain(s) :
Informatique [cs]/Automatique
English abstract : [en]
This paper proposes a continuous-time framework for the least-squares parameter estimation method through evolution equations. Nonlinear systems in the standard state space representation that are linear in the unknown, ...
Show more >This paper proposes a continuous-time framework for the least-squares parameter estimation method through evolution equations. Nonlinear systems in the standard state space representation that are linear in the unknown, constant parameters are investigated. Two estimators are studied. The first one consists of a linear evolution equation while the second one consists of an impulsive linear evolution equation. The paper discusses some theoretical aspects related to the proposed estimators: uniqueness of a solution and an attractive equilibrium point which solves for the unknown parameters. A deterministic framework for the estimation under noisy measurements is proposed using a Sobolev space with negative index to model the noise. The noise can be of large magnitude. Concrete signals issued from an electronic device are used to discuss numerical aspects.Show less >
Show more >This paper proposes a continuous-time framework for the least-squares parameter estimation method through evolution equations. Nonlinear systems in the standard state space representation that are linear in the unknown, constant parameters are investigated. Two estimators are studied. The first one consists of a linear evolution equation while the second one consists of an impulsive linear evolution equation. The paper discusses some theoretical aspects related to the proposed estimators: uniqueness of a solution and an attractive equilibrium point which solves for the unknown parameters. A deterministic framework for the estimation under noisy measurements is proposed using a Sobolev space with negative index to model the noise. The noise can be of large magnitude. Concrete signals issued from an electronic device are used to discuss numerical aspects.Show less >
Language :
Anglais
Popular science :
Non
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