Uncertainty model unfalsification

The main contributions presented are (i) to widen the classes of model sets for which necessary and sufficient conditions for uncertainty model unfalsification can be obtained, and (ii) to display the effect of different assumptions concerning the modeling error on the curves defining the boundary o...

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Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 1; pp. 163 - 168 vol.1
Main Authors Kosut, R.L., Anderson, B.D.O.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Adaptive control
Adaptive systems
Australia
Contracts
Displays
Mathematics
Predictive models
Robustness
Testing
Uncertainty
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ISBN0780341872
9780780341876
ISSN0191-2216
DOI10.1109/CDC.1997.650608

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Abstract The main contributions presented are (i) to widen the classes of model sets for which necessary and sufficient conditions for uncertainty model unfalsification can be obtained, and (ii) to display the effect of different assumptions concerning the modeling error on the curves defining the boundary of unfalsified models (the optimal uncertainty tradeoff curve) for the same underlying data set.
AbstractList The main contributions presented are (i) to widen the classes of model sets for which necessary and sufficient conditions for uncertainty model unfalsification can be obtained, and (ii) to display the effect of different assumptions concerning the modeling error on the curves defining the boundary of unfalsified models (the optimal uncertainty tradeoff curve) for the same underlying data set.
Author Kosut, R.L.
Anderson, B.D.O.
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Snippet The main contributions presented are (i) to widen the classes of model sets for which necessary and sufficient conditions for uncertainty model unfalsification...
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StartPage 163
SubjectTerms Adaptive control
Adaptive systems
Australia
Contracts
Displays
Mathematics
Predictive models
Robustness
Testing
Uncertainty
Title Uncertainty model unfalsification
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