نتایج جستجو برای: Data uncertainty
تعداد نتایج: 2495003 فیلتر نتایج به سال:
in this thesis a calibration transfer method is used to achieve bilinearity for augmented first order kinetic data. first, the proposed method is investigated using simulated data and next the concept is applied to experimental data. the experimental data consists of spectroscopic monitoring of the first order degradation reaction of carbaryl. this component is used for control of pests in frui...
abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...
for efficiency evaluation of some of the decision making units that have uncertain information, rough data envelopment analysis technique is used, which is derived from rough set theorem and data envelopment analysis (dea). in some situations rough data alter nonradially. to this end, this paper proposes additive rough–dea model and illustrates the proposed model by a numerical example.
uncertainty in the financial market will be driven by underlying brownian motions, while the assets are assumed to be general stochastic processes adapted to the filtration of the brownian motions. the goal of this study is to calculate the accumulated wealth in order to optimize the expected terminal value using a suitable utility function. this thesis introduced the lim-wong’s benchmark fun...
this paper proposes a family of robust counterpart for uncertain linear programs (lp) which is obtained for a general definition of the uncertainty region. the relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. those properties lead us to characterize primal and dual robust counterparts. the researchers show t...
spectral-based subspace learning is a common data preprocessing step in many machine pipelines. The main aim to learn meaningful low dimensional embedding of the data. However, most methods do not take into consideration possible measurement inaccuracies or artifacts that can lead with high uncertainty. Thus, directly from raw be misleading and negatively impact accuracy. In this paper, we prop...
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