Add to ORKGAbstract: The paper describes and evaluates an optimal instrumental variable method for identifying hybrid continuous-time transfer function models of the Box-Jenkins form from discrete-time sampled data, where the relationship between the measured input and output is a continuous-time transfer function, while the noise is represented as a discrete-time AR or ARMA process. The performance of the proposed hybrid parameter estimation scheme is evaluated by Monte Carlo simulation analysis. Copyright c©2006 IFA
This paper considers the problem of continuous-time model identification from non-uniformly sampled ...
International audienceThe off-line estimation of the parameters of continuous-time, linear, time-inv...
Identification of Linear Parameter-Varying (LPV) models is often addressed in an Input-Output (IO) s...
This paper describes the unified Refined Instrumental Variable approach to the time domain identific...
This article presents instrumental variable methods for direct continuous-time estimation of a Hamme...
International audienceThis study investigates the estimation of continuous-time Box-Jenkins model pa...
Abstract: This paper describes an optimal instrumental variable method for identifying discrete-time...
For many years, various methods for the identification and estimation of parameters in linear, discr...
In this paper, the instrumental variable (IV) and expectation-maximization (EM) methods are combined...
Abstract: This paper describes optimal instrumental variable methods for identifying discrete-time t...
This study presents in a new unified way, optimal instrumental variable methods for identifying disc...
Identification of time-continuous models from sampled data is a long standing topic of discussion, a...
The identification of linear parameter-varying systems in an input–output setting is investigated, f...
For many years, various methods for the identification and estimation of parameters in linear, discr...
Cette thèse traite de l’identification de systèmes dynamiques à partir de données échantillonnées à ...
This paper considers the problem of continuous-time model identification from non-uniformly sampled ...
International audienceThe off-line estimation of the parameters of continuous-time, linear, time-inv...
Identification of Linear Parameter-Varying (LPV) models is often addressed in an Input-Output (IO) s...
This paper describes the unified Refined Instrumental Variable approach to the time domain identific...
This article presents instrumental variable methods for direct continuous-time estimation of a Hamme...
International audienceThis study investigates the estimation of continuous-time Box-Jenkins model pa...
Abstract: This paper describes an optimal instrumental variable method for identifying discrete-time...
For many years, various methods for the identification and estimation of parameters in linear, discr...
In this paper, the instrumental variable (IV) and expectation-maximization (EM) methods are combined...
Abstract: This paper describes optimal instrumental variable methods for identifying discrete-time t...
This study presents in a new unified way, optimal instrumental variable methods for identifying disc...
Identification of time-continuous models from sampled data is a long standing topic of discussion, a...
The identification of linear parameter-varying systems in an input–output setting is investigated, f...
For many years, various methods for the identification and estimation of parameters in linear, discr...
Cette thèse traite de l’identification de systèmes dynamiques à partir de données échantillonnées à ...
This paper considers the problem of continuous-time model identification from non-uniformly sampled ...
International audienceThe off-line estimation of the parameters of continuous-time, linear, time-inv...
Identification of Linear Parameter-Varying (LPV) models is often addressed in an Input-Output (IO) s...