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Identification of parametric models : from experimental data
Eric Walter,Luc Pronzato +1 more
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The article was published on 1997-01-01 and is currently open access. It has received 1251 citations till now. The article focuses on the topics: Parametric model & Experimental data.read more
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Proceedings ArticleDOI
Robust data processing in the presence of uncertainty and outliers: Case of localization problems
TL;DR: In some situations in which efficient semi-heuristic methods are known, the methodology leads to a justification of these efficient heuristics - which makes the authors confident that the new methods will be efficient in other situations as well.
Proceedings ArticleDOI
Guaranteed characterization of exact confidence regions for FIR models under mild assumptions on the noise via interval analysis
Michel Kieffer,Eric Walter +1 more
TL;DR: The aim of the present paper is to show how interval analysis can contribute to a guaranteed characterization of exact confidence regions in large-scale problems through the estimation of the parameters of finite-impulse-response models.
Dissertation
Etude de la fonctionnalisation de polyuréthannes : effet du spiropyranne sur leurs propriétés optiques et mécaniques
TL;DR: In this article, the authors describe a photochromique of six different types of polyurethane photochromiques with spiropyrane, which can be used to evaluate, signaliser, and reagir a un changement de couleur and de spectre dabsorbance.
Proceedings ArticleDOI
Towards nonlinear model predictive control with integrated experiment design
TL;DR: This paper extends the formulation for the integration of experiment design of linear dynamic systems to nonlinear dynamic systems resulting in a NMPC formulation with integrated experiment design (iED-NMPC), and proposes to reformulate this nonlinear matrix inequality using Sylvester's criterion.
Journal ArticleDOI
Identifiability of Stochastically Modelled Reaction Networks
TL;DR: It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters.