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Aiming at disposing problems in uncertain decision system, a new rough set model based on decision rule is presented. The model is proposed based on Pawlak rough set, Probabilistic rough set, variable precision rough set and Bayes rough set, and its parameters use golden-section for reference. Then some properties of the model are discussed.
Suppose that we have a set of emissions reduction technologies whose greenhouse gas abatement potential is unknown, and we wish to find an optimal portfolio (subset) of these technologies. Due to the interaction between technologies, the effectiveness of a portfolio can only be observed through expensive field implementations. We view this problem as an online optimal learning problem with correlated...
This paper extends FSM, a recently proposed semantic data model that supports fuzziness, imprecision and uncertainty of real-world. More precisely, the paper proposes four new concepts, decisional grouping, inhibition, multiplicity and selection, which allows enhancing the modeling of real-world applications. It integrates these concepts in FSM by the definition of new decision rules.
This paper is about fault diagnosis rules. Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. Furthermore, considering the constraint of fault diagnosis cost, a dynamic optimization method on the...
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