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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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Citations
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Optimal Control for Learning with Applications in Dynamic MRI

John Maidens
TL;DR: New approaches to solving optimal control algorithms based on convex relaxations, and exploiting geometric structure in the underlying optimization problem are presented, closing the path from experiment design to data collection to synthesis of interpretable information.
Dissertation

Modelling, analysis and control for systems biology : application to bacterial growth models

Alfonso Carta
TL;DR: It is shown that the proposed qualitative con- trol strategies, which act on the gene expression machinery (GEM) can mathematically control the cell growth rate, by means of phase plane analysis and bifurcation diagrams.
Book ChapterDOI

Optimal Training Sequences for Locally Recurrent Neural Networks

TL;DR: The problem of determining an optimal training schedule for a locally recurrent neural network is discussed and the proper choice of the most informative measurement data guaranteeing the reliable prediction of the neural network response is considered.
DissertationDOI

Structural identifiability of large systems biology models

D. Joubert
TL;DR: A computationally efficient algorithm well suited to the analysis of large models, in the model development process is introduced that allows for unknown initial conditions to be parameterised and accordingly, repameterisations requiring the transformation of states associated with unidentifiable initial conditions, can easily be obtained.
Journal ArticleDOI

Anwendung der modellbasierten Versuchsplanung zur Ermittlung von Kinetikparametern bei Grenzflächenreaktionen

TL;DR: In this article, the authors present an approach to optimale Versuchsplanung, e.g., optimally bestimmen the kinetischen Parameter der chemischen Reaktion.