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.
Lien vers l'élément du SI MIA
http://genome.jouy.inra.fr/logiciels/filtrex/ Informations générales
Statut
À disposition Partenaire externe
aucun Manuel de référence
Rapport technique MaIAGE: 2009-3 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
Etat
Développement arrêté 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
Publication de référence