Probabilistic bounds for model invalidation assessment

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

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Original languageEnglish
Article numberTuA11.3
Pages (from-to)376-381
Journal / PublicationProceedings of the IEEE Conference on Decision and Control
Publication statusPublished - 2004
Externally publishedYes


Title2004 43rd IEEE Conference on Decision and Control (CDC)
Period14 - 17 December 2004


This paper is concerned with a mixed deterministic/probabilistic model invalidation problem, which amounts to determining the probability for a given model to reproduce some given experimental data. We consider an additive uncertain model, in which the modelling uncertainty is characterized in time domain by the li induced system norm. The data available for Invalidation are input-output time series and are assumed to have been corrupted by a random noise with Gaussian distribution. Fbr a given uncertainty norm bound, our objective is to compute the probability for no uncertainty to exist that may satisfy the prescribed bound and match the input-output measurements. While the exact computation of this probability may pose a formidable task, we derive its upper and lower bounds.

Research Area(s)

  • ℓ 1 norm, Gaussian distribution, Probabilistic model invalidation, Uncertain model