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Identification of parametric models : from experimental data

Eric Walter, +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.

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Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles

TL;DR: The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes and to suggest metabolic engineering strategies to improve byproduct secretion.
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Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties

TL;DR: In this article, asymptotic designs and uniform convergence of LS estimators are discussed. But the authors focus on the small-sample precision of the LS estimator, not on the optimality of the estimator itself.
Journal ArticleDOI

Unscented Kalman Filter: Aspects and Adaptive Setting of Scaling Parameter

TL;DR: A novel method for adaptive setting of the parameter in the unscented Kalman filter for state estimation of nonlinear stochastic dynamic systems with a special focus on the scaling parameter of the filter is proposed.
Journal ArticleDOI

Computational procedures for optimal experimental design in biological systems

TL;DR: A Monte-Carlo-based identifiability analysis is suggested for the sake of comparing among different experimental schemes, and the use of a robust global nonlinear programming solver is proposed.
Journal ArticleDOI

Tension control for winding systems with two-degrees-of-freedom H/sub /spl infin// controllers

TL;DR: In this paper, the authors present a robust control with two degrees of freedom (2DOF) and gain scheduling for web transport systems, where the main concern is to control independently speed and tension in spite of perturbations such as radius variations and changes of setting point.