Skip to Main content Skip to Navigation
Reports

Experiment Selection for the Discrimination of Semi-Quantitative Models of Dynamical Systems

Ivayla Vatcheva 1 Olivier Bernard 2 Hidde de Jong Nicolaas Mars
1 HELIX - Computer science and genomics
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
2 COMORE - Modeling and control of renewable resources
LOV - Laboratoire d'océanographie de Villefranche, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for experiment selection that helps in discriminating between differential equation models of experimental systems in a systematic and efficient way. The method generalizes upon previous work on model discrimination in that it deals with semi-quantitative differential equations, which use interval bounds on parameter values and envelopes for functional relations. The model discrimination method is based on an entropy criterion for the selection of the most informative experiment. The applicability of the method to real-world problems is illustrated by means of an example in population biology, the discrimination of competing models of the growth of phytoplankton in a bioreactor.
Document type :
Reports
Complete list of metadata

https://hal.inria.fr/inria-00071639
Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 6:22:52 PM
Last modification on : Wednesday, December 9, 2020 - 3:11:25 PM
Long-term archiving on: : Sunday, April 4, 2010 - 10:30:30 PM

Identifiers

  • HAL Id : inria-00071639, version 1

Citation

Ivayla Vatcheva, Olivier Bernard, Hidde de Jong, Nicolaas Mars. Experiment Selection for the Discrimination of Semi-Quantitative Models of Dynamical Systems. RR-4940, INRIA. 2003. ⟨inria-00071639⟩

Share

Metrics

Record views

353

Files downloads

451