Approximate viability using quasi-random samples and a neural network classifier
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Author / Producer
Date
2008
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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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.
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Publication status
published
Editor
Book title
Proceedings of the 17th IFAC World Congress
Journal / series
Volume
41 (2)
Pages / Article No.
14342 - 14347
Publisher
Elsevier
Event
17th IFAC World Congress (IFAC 2008)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
neural network; viability; quasi-random technique
Organisational unit
03751 - Lygeros, John / Lygeros, John