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Interval Methods for Model Qualification: Methodology and Advanced Application

Julien Alexandre Dit Sandretto 1 Gilles Trombettoni 2 David Daney 3 
2 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 Phoenix - Programming Language Technology For Communication Services
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, EA4136 - Handicap et système nerveux :Action, communication, interaction: rétablissement de la fonction et de la participation [Bordeaux]
Abstract : An actual model in simulation (e.g. in chemistry) or control (e.g. in robotics) is often too complex to use, and sometimes impossible to obtain. To handle a system in practice, a simplification of the real model is often necessary. This simplification goes through some hypotheses made on the system or the modeling approach. These hypotheses are rarely verified whereas they could lead to an inadmissible model, over approximated for its use. In this paper, we propose a method that qualifies the simplification validity for all models that can be expressed by real-valued variables involved in closed-form relations and depending on parameters. We based our approach on a verification of a quality threshold on the hypothesis relevance. This method, based on interval analysis, checks the acceptance of the hypothesis in a full range of the whole model space, and gives bounds on the quality threshold and on the model parameters. Our approach is experimentally validated on a robotic application.
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Submitted on : Friday, May 28, 2021 - 11:21:32 AM
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Julien Alexandre Dit Sandretto, Gilles Trombettoni, David Daney. Interval Methods for Model Qualification: Methodology and Advanced Application. Mathematics in Computer Science, 2014, 8 (3-4), pp.479-493. ⟨10.1007/s11786-014-0210-0⟩. ⟨hal-01057364⟩



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