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Підхід до підтримки прийняття рішень операторами складних технологічних процесів

Підхід до підтримки прийняття рішень операторами складних технологічних процесів

Abstract

The article represents a new approach to building decision support system based on modified counter propagation neural network. The decision support system is able to recognize the current state of the maintained system giving recommendations concerned to further control influence. It is a self-training system, which can be successfully trained on small data sets. У статті представлено новий підхід до побудови системи підтримки прийняття рішень, яка базується не нейронній мережі зустрічного розповсюдження. Розроблена система підтримки прийняття рішень здатна розпізнавати поточний стан об’єкта керування, даючи рекомендації щодо керуючого впливу. Система здатна до самонавчання та може бути навчена на невеликих об’ємах даних. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/464

Related Organizations
Keywords

information technology management, технологічні процеси, інформаційні технології управління, технологические процессы, информационные технологии управления, technological processes

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    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.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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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