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Исследование показателей надежности систем анализа информации на основе модели поглащающей цепи Маркова

Исследование показателей надежности систем анализа информации на основе модели поглащающей цепи Маркова

Abstract

В результате исследования алгоритма функционирования модуля раннего выявления сверхнормативных потерь доказана возможность его моделирования с помощью поглощающих сетей Маркова. Особый интерес представляет исследование вероятностных характеристик алгоритма функционирования модуля раннего выявления потерь на предмет выявления взаимосвязи показателей надежности отдельных элементов или вероятности наступления отдельных событий и вероятности передачи достоверной информации. Выявленные в ходе анализа зависимости позволяют установить пороговые значения надежностных характеристик узлов системы.

Keywords

энергоэффективность, Энергетика, Энергосбережение, Математика, управление, моделирование процессов энергоснабжения.

  • BIP!
    Impact byBIP!
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    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green