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Reachability Computations: approximate solution using neural networks and randomized techniques

This presentation deals with the problem of computing reachability sets for nonlinear continuous or hybrid systems. The main objective is to beat the curse of dimensionality. In other words, the goal is to avoid the exponential growth of required computational resource with respect to the dimension of the system. A randomized approach and Neural networks for reachability computation is proposed:it avoids gridding the state-space, and uses random extraction of points instead. A result of convergence for the neural networks approximator is shown. These techniques have been implemented successfully for linear and nonlinear systems.

Type of Seminar:
IfA Seminar
Badis Djeridane
May 24, 2007   11am

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