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Apprentissage et adaptation pour la modélisation stochastique de systèmes dynamiques réels

Abstract : The exploitation of Artificial Intelligence algorithms in real conditions is an interesting method for their improvement, since weaknesses are shown very quickly thanks to real, uncontrolled constraints. In particular, I study two problems of medical diagnosis and a classical problem of robot navigation. Using fuzzy sets with Markov models provide an intuitive but powerful system to solve such situations. Then, I introduce diagnosis learning which betters the cooperation with doctors, as it allows correcting the model while ensuring numerical stability. Thus, doctors can modify the patient model without setting each parameter manually. Finally, I show this approach can be generalized to a whole class of diagnosis problems. To achieve this goal, I show an integrated development environment that allows to simply link modules altogether to have a given problem solved. This should help creating new applications, while minimizing the programming time of researchers.
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Submitted on : Thursday, March 29, 2018 - 10:46:35 AM
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Laurent Jeanpierre. Apprentissage et adaptation pour la modélisation stochastique de systèmes dynamiques réels. Autre [cs.OH]. Université Henri Poincaré - Nancy 1, 2002. Français. ⟨NNT : 2002NAN10246⟩. ⟨tel-01746870⟩



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