Results 231 to 240 of about 547,284 (267)

Portfolio Insurance and model uncertainty

OR Spectrum, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Modeling Uncertainty in Banking Networks

2014
Recent evidence from simulations of banking networks suggests that properties of the network design such as connectivity, bank size, or concentration affect networks’ ability to withstand stress (Arinaminpathy et al. 2012; Gai at al. 2011). However, those studies typically assume that all banks have complete knowledge about the whole system.
Stojan Davidovic, Mirta Galesic, Konstantinos V. Katsikopoulos, Nimalan Arinaminpathy   +3 more
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Modeling in the presence of switching uncertainties

Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference [Cat. No. 00CH37066], 2001
This paper presents a method that takes into account the switching uncertainties in the signal path of the measurement devices during the identification of device models. Switching phenomena in the measurement instrument, like switching attenuators, induce jumps in the measured input-output characteristic that can be much larger than the noise ...
Wendy Van Moer, Yves Rolain, Rik Pintelon   +2 more
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Modeling of uncertainty

Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, 2016
In this paper, we propose a new method of ontology fuzzification which is able to analyzing data imperfection. In general, the constituents of an ontology are, as all data from the real world, characterized by aspects of inaccuracies and uncertainties.
Houda Akremi, Sami Zghal, Gayo Diallo
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Finding an LFT uncertainty model with minimal uncertainty

2013 European Control Conference (ECC), 2013
In this paper, we present a procedure for finding the best LFT uncertainty model by minimizing the ℌ -infinity norm of the uncertainty set with respect to a nominal model subject to known input-output data. The main problem is how to express the data-matching constraints for convenient use in the optimization problem.
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Modelling positioning uncertainties

1989
Robot programs generated by a human or by an automatic planner, must execute in a real environment which differs slightly from the model used at programming time. Thus we need to represent this uncertainty. This representation can be used at programming time to directly produce a valid program, or to verify the validity of a program afterwards.
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Modelling uncertainty

Information Sciences, 2010
Javier Montero, Da Ruan 0001
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Uncertainty and Uncertainty Propagation in Soil Mapping and Modelling

2018
In 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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Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial

Mechanical Systems and Signal Processing, 2023
Michael Beer, Sifeng Bi, John Mottershead   +2 more
exaly  

Decomposing crop model uncertainty: A systematic review

Field Crops Research, 2022
Tomáš A Remenyi, Neil Huth, Rebecca Harris   +2 more
exaly