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Approximate Viability Using Quasi-Random Samples and a Neural Network Classifier

Author(s):

J. Lygeros, B. Djeridane
Conference/Journal:

IFAC World Congress, vol. 17, pp. 14342-14347, vol.17, part I
Abstract:

We propose a novel approach to the computational investigation of reachability properties for nonlinear control systems. Our goal is to combat the curse of dimensionality, by proposing a mesh-free algorithm to numerically approximate the viability kernel of a given compact set. Our algorithm is based on a non-smooth analysis characterization of the viability kernel. At its heart is a neural network classifier based on Bayesian regularization, which operates on a pseudorandom sample extracted from the state-space (instead of a regular grid). The algorithm was implemented in Matlab and applied successfully to examples with linear and nonlinear dynamics.

Year:

2008
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { LygDje:2008:IFA_3242,
    author={J. Lygeros and B. Djeridane},
    title={{Approximate Viability Using Quasi-Random Samples and a
	  Neural Network Classifier}},
    booktitle={IFAC World Congress},
    pages={14342--14347},
    year={2008},
    address={},
    month=jul,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3242}
}
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