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Identification of Parametric Models: from Experimental Data
- Publisher
- Springer Berlin Heidelberg
- Copyright
- © Springer-Verlag Berlin Heidelberg 2011
- ISBN
- 978-3-642-15955-8
- Pages
- 117 –128
- DOI
- 10.1007/978-3-642-15956-5_6
- Publisher site
- See Chapter on Publisher Site
Abstract
[Interval constraint propagation methods have been shown to be efficient, robust and reliable to solve difficult nonlinear bounded-error state estimation problems. However they are considered as unsuitable in a probabilistic context, where the approximation of a probability density function by a set cannot be accepted as reliable. This paper proposes a new probabilistic approach which makes it possible to use classical set-membership observers which are robust with respect to outliers. The approach is illustrated on a localization of robots in situations where there exist a large number of outliers.]
Published: May 13, 2011