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Uncertainty propagation in dynamical systems
Automatica, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Igor Mezic, Thordur Runolfsson
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Patterns for Implementing Uncertainty Propagation
Proceedings of the 23rd European Conference on Pattern Languages of Programs, 2018In this paper, the design patterns Uncertain Number and Propagation Strategy are presented. They are useful for storing uncertainties of values and propagating them throughout calculations in an application. Uncertain Number represents a numerical value and its respective uncertainty. Propagation Strategy represents the propagation method, to correctly
Michael Krisper +3 more
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Uncertainty Propagation in Biomedical Models
2015Mathematical models are prevalent in modern medicine. However, reasoning with realistic biomedical models is computationally demanding as parameters are typically subject to nonlinear relations, dynamic behavior, and uncertainty. This paper addresses this problem by proposing a new framework based on constraint programming for a sound propagation of ...
Andrea Franco +2 more
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2016
Propagation of uncertainty is a fundamental concept in several aspects related to modeling.
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Propagation of uncertainty is a fundamental concept in several aspects related to modeling.
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Propagating Uncertainty in Instrumentation Systems
IEEE Transactions on Instrumentation and Measurement, 2005An algorithm for propagating measurement uncertainty in a system of interconnected modules is presented. The method adheres strictly to current best-practice in the evaluation and reporting of measurement uncertainty. It allows modular instrumentation systems to be designed that will propagate uncertainty automatically. The algorithm is simple, general,
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Uncertainty and Uncertainty Propagation in Soil Mapping and Modelling
2018In previous chapters, the use of geostatistical modelling for soil mapping was addressed. We learnt that one of the advantages of kriging is that it not only produces a map of predictions but that it also quantifies the uncertainty about the predictions, through the kriging standard deviation. In this chapter we will look into this in more detail.
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Propagation of Uncertainty An Uncertainty Budget Example
2014Finding the uncertainty of a single measurement is just part of the story. In this chapter we show how to combine the uncertainties from several measurements to determine the total uncertainty for a calculated quantity. For example, while generating a cost estimate for repaving a rectangular parking lot, you may need to combine the uncertainties in ...
Colin Ratcliffe, Bridget Ratcliffe
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Propagation of uncertainty in dependence graphs
1991In this paper the main results of DRUMS R.P3.3 Task ’Propagation of Uncertainty in Dependence Graphs’ are presented. It is divided in two parts. In the first one, it is considered a new calculus with imprecise probabilities, which is not as uninformative as classical one, [6, 7, 9]. In the second one, the axiomatic squeme given in [11] has been adapted
José E. Cano +2 more
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2011
Quite often the quantity under measurement (measurand) Yis not measured directly, but is the result of measurement of several independent quantities X1, X2, X3, …, X n . The measurand Yis also called the output quantity and X1, X2, X3, …, X n as input quantities. The quantity Yis related to output quantities through some well-defined relation.
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Quite often the quantity under measurement (measurand) Yis not measured directly, but is the result of measurement of several independent quantities X1, X2, X3, …, X n . The measurand Yis also called the output quantity and X1, X2, X3, …, X n as input quantities. The quantity Yis related to output quantities through some well-defined relation.
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Orbital Uncertainty Propagation with PC-Kriging
2020 American Control Conference (ACC), 2020In this paper, the polynomial chaos based Kriging (PC-Kriging) is utilized as a surrogate model for orbital uncertainty propagation. The polynomial chaos can represent the global trend of the uncertainty distribution whereas the Kriging captures the local uncertainty variations. A new learning strategy is proposed to incrementally build and improve the
Bin Jia, Ming Xin 0001
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