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The flexible structure of the Mamdani-based fuzzy logic allows to easily model complex uncertainties in the navigation and drug delivery decision making of the bionanorobots.
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After that, the nonlinearity is modelled as a complex uncertainty lying inside a small-gain cone.
The numerical treatment of the stochastic multiscale models can be very challengeable as the existence of complex uncertainty and multiple physical scales in the models.
This chapter addresses modeling complex decision settings in which multiple uncertainties affect the performance metrics, decision trees, and influence diagrams.
In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems.
This study aims to assess how accurately and efficiently HDMR/FHDMR based response surface techniques can capture complex model output uncertainty.
Other important levels of uncertainty include model uncertainty and generalizability uncertainty (6).
The dynamical network is a complex network model containing uncertainty and coupling delay, where delay appears in a complex network but not in the isolate systems.
Sampling uncertainty and model uncertainty are two epistemic uncertainty sources.
However, the RANS predictions have large model-form uncertainties for many complex flows, e.g., those with non-parallel shear layers or strong mean flow curvature.
We used bootstrapping and probabilistic sensitivity analysis to incorporate sampling uncertainty, parameter uncertainty and model uncertainty.
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