Add to ORKGNational audienceThis paper is devoted to a short presentation of the use we did of nonparametric estimation theory for the estimation, filtering and control of un- certain dynamic systems. The fundamental advantage of this approach is its low dependence from any a priori modeling assumptions about uncertain dynamic com- ponents. It appears to be of great interest for the control of general discrete-time processes, and in particular biotechnological processes, which are emblematic of nonlinear uncertain and partially observed systems
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of t...
New methods for statistical process control are presented, where the inferences have a nonparametric...
The paper deals with the control algorithms for discrete delayed systems with unknown inputs (distur...
National audienceThis paper is devoted to a short presentation of the use we did of nonparametric es...
This article is devoted to a presentation of the authors' practice of the non-parametric estimation ...
We propose a new probabilistic framework for nonparametric identification and estimation of dynamic ...
In this paper, we propose a nonparametric method for monitoring and controlling nonlinear systems wh...
This paper presents a mathematical framework for state estimation of dynamic systems for which only ...
Abstract: We consider the problem of state and parameter reconstruction for uncertain dynamical syst...
We consider the problem of state and parameter reconstruction for uncertain dynamical systems that c...
AbstractThe nonlinear filtering problem of estimating the state of a linear stochastic system from n...
Abstract: We consider the problem of state and parameter reconstruction for uncertain dynamical syst...
This paper outlines a new approach to the statistical identification and estimation of dynamic model...
Let (Xt), be valued stochastic process defined by a discrete time dynamical system as Xt = [phi](Xt-...
The estimation of unmeasured state and parameters for complex systems is of great importance for app...
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of t...
New methods for statistical process control are presented, where the inferences have a nonparametric...
The paper deals with the control algorithms for discrete delayed systems with unknown inputs (distur...
National audienceThis paper is devoted to a short presentation of the use we did of nonparametric es...
This article is devoted to a presentation of the authors' practice of the non-parametric estimation ...
We propose a new probabilistic framework for nonparametric identification and estimation of dynamic ...
In this paper, we propose a nonparametric method for monitoring and controlling nonlinear systems wh...
This paper presents a mathematical framework for state estimation of dynamic systems for which only ...
Abstract: We consider the problem of state and parameter reconstruction for uncertain dynamical syst...
We consider the problem of state and parameter reconstruction for uncertain dynamical systems that c...
AbstractThe nonlinear filtering problem of estimating the state of a linear stochastic system from n...
Abstract: We consider the problem of state and parameter reconstruction for uncertain dynamical syst...
This paper outlines a new approach to the statistical identification and estimation of dynamic model...
Let (Xt), be valued stochastic process defined by a discrete time dynamical system as Xt = [phi](Xt-...
The estimation of unmeasured state and parameters for complex systems is of great importance for app...
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of t...
New methods for statistical process control are presented, where the inferences have a nonparametric...
The paper deals with the control algorithms for discrete delayed systems with unknown inputs (distur...