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Communication dans un congrès

Inner and outer capture basin approximation with support vector machines

Abstract : We propose a new approach to solve target hitting problems, that iteratively approximates capture basins at successive times, using a machine learning algorithm (as a particular case, we use Support Vector Machines) trained on points of a grid with boolean labels. We consider two variants of the approximation (from inside and from outside), and we state the conditions on the machine learning procedure that guarantee that the approximations converge to the actual capture basin when the resolution of the grid decreases to 0. Moreover, we define a control procedure which uses the set of capture basin approximations to drive a point into the target. When using the inner approximation, the procedure guarantees to hit the target, and when the resolution of the grid tends to 0, the controller tends to the optimal one (minimizing time to hit the target). SVMs provide parcimonious approximations, used to derive fast controllers. We illustrate the method on two simple examples, Zermelo and car on the hill problems.
Type de document :
Communication dans un congrès
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Déposant : Migration Irstea Publications <>
Soumis le : vendredi 15 mai 2020 - 21:08:26
Dernière modification le : jeudi 8 octobre 2020 - 17:06:02


  • HAL Id : hal-02596548, version 1
  • IRSTEA : PUB00034525



L. Chapel, Guillaume Deffuant. Inner and outer capture basin approximation with support vector machines. 8th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2011, Jul 2011, Noordwirjkerhout, Netherlands. pp.6. ⟨hal-02596548⟩



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