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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
Global identification of mechanical and electrical parameters of DC motor driven joint with a fast CLOE method
TL;DR: A technique which mixes a closed loop output error method with the inverse dynamic identification model method which allows using linear least-squares technique to estimate the parameters and shows the effectiveness of the new procedure.
Book ChapterDOI
Nonlinear bounded-error parameter estimation using interval computation
L. Jaulin,E. Walter +1 more
TL;DR: The aim of the method presented is to characterize the set S of all values of the parameter vector that are acceptable in the sense that all errors between the experimental data and the corresponding model outputs lie between known lower and upper bounds.
Proceedings ArticleDOI
System identification of the intracellular photoreaction process induced by photodynamic therapy
TL;DR: A new approach is proposed to estimate photophysical parameters which characterize the ability of a photosensitizing drug to produce singlet oxygen, based on system identification techniques and can be directly applied to in vivo data.
Book ChapterDOI
An Optimal Scanning Sensor Activation Policy for Parameter Estimation of Distributed Systems
TL;DR: A technique is proposed to solve an optimal node activation problem in sensor networks whose measurements are supposed to be used to estimate unknown parameters of the underlying process model in the form of a partial differential equation using a simplicial decomposition algorithm.
Journal ArticleDOI
On the effect of sampling rate and experimental noise in the discrimination between microbial growth models in the suboptimal temperature range
TL;DR: This work deals with secondary models describing microbial kinetics in the suboptimal temperature range and their possibility to be discriminated, using the method of Optimal Experiment Design for Model Discrimination to investigate the practical (in)feasibility of model discrimination given different noise and sampling frequency values.