FILTREX


FILTREX is a User-friendly Software for Parametric Identification, Model Comparison and Optimal Sequential Sampling of Experiments of Complex Microbiological Dynamic Systems by Nonlinear Filtering. It is written in Matlab©.

IN WHICH CONTEXT YOU CAN USE FILTREX ?

In the microbiological context of modelisation of complex microbiological dynamic systems characterised by:

  • One or several bacteria species leading eventually to several simultaneous dynamic equations.
  • The growth or the inactivation are not directly observable.
  • Sophisticated dilution and counting stepwise processes associated to several experimental errors.
FILTREX OBJECTIVES
  • Parameter identification of the growth and inactivation models.
  • Comparison and selection of these models.
  • Optimal sequential sampling of experiments (three options).
FILTREX MATHEMATICAL FRAMEWORK
  • A nonlinear particle filtering method based on a new nonlinear particular (nonparametrical) filtering technique using a convolution kernel approach and a particular resampling trick.
  • Nonlinear autoregressive dynamic systems simultaneously defined by a stochastic state equation and an observation equation.
  • Not directly observable systems.
  • Non explicite likelihood function.
FILTREX ADVANTAGES
  • No initial guesses for parameters are needed: postulated parameter intervals are only necessary (they can be broad in a first step if only very few information is available on parameters).
  • The experimental errors (samplings, countings, ...) are better taken into account, and their coefficients of variation can be also estimated.
  • It is based on published theoretical results (convergence, ...).
  • In further releases, several species will be simultaneously considered.


Informations générales
Partenaire externe
aucun
Suivi
Maintenu
Informations spécifiques
Langage(s) de développement
Langage(s) d'interface
N° de version courante
3.0 (Matlab R2015a)
Date de la version courante
OS supporté
Type de licence


Porteur(s)
Unité
MaIAGE
Equipe
Dynenvie
Auteur(s)
Gauchi Jean-Pierre
Bouvier Annie
Bidot Caroline
Choquet R. (CNRS/CEFE, Montpellier)
Rossi Vivien (UMR MISTEA Montpellier)
Vila Jean-Pierre (UMR MISTEA Montpellier)
Contact
jean-pierre.gauchi@inra.fr


 

 

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