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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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Dissertation
Caractérisation au moyen d'outils mathématiques des effets vasculaires du bevacizumab à des fins d'optimisation des protocoles thérapeutiques dans le cas des tumeurs cérébrales
Alaoui Lasmaili,Karima El +1 more
TL;DR: In this paper, the authors propose an algorithm for traitement d'images of reseau vasculaire tumoral acquired by microscopie intravitale, with the objective of caracterising the effets of anti-VEGF Bevacizumab (Avastin) in vivo.
Dissertation
Nonparametric identification of linear time-varying systems
TL;DR: System identification is a tool which allows the user to build models of dynamic systems from experimental noisy data which connects the world of control theory, data acquisition, signal processing, statistics, time series analysis and many other areas.
Journal ArticleDOI
A kriging interpolation strategy for the optimization of Acidithiobacillus ferrooxidans biomass production using fed-batch bioreactors
R.H. Milocco,M.N. Gatti +1 more
TL;DR: In this paper, an optimal spatial interpolation of experimental data is proposed for the optimization of acidithiobacillus ferrooxidans biomass production in fed-batch reactors.
Book ChapterDOI
Time Accounting Artificial Neural Networks for Biochemical Process Models
TL;DR: The proposed hybrid (dynamical ANN and analytical submodel) schemes are promising modeling framework when the process is strongly nonlinear and particularly when input--output data is the only information available.
Posted Content
Construction of ODE systems from time series data by a highly flexible modelling approach
TL;DR: A down-to-earth approach to purely data-based modelling of unknown dynamical systems is presented, determining the unknown right-hand side f(t,y) from some trajectory data y_k(t_j), possibly very sparse, is given.