Decrease of time of model synthesis in intellectual monitoring systems

The article investigates modern intellectual monitoring systems (IMS), which are able to predict the consequences of the adopted control decisions of decision support systems (DSS), thanks to the modeling of the characteristics of monitored objects. The drawbacks of existing implementations of IMS s...

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Bibliographic Details
Date:2019
Main Authors: Avramenko, A.S., Golub, S.V.
Format: Article
Language:English
Published: Інститут проблем математичних машин і систем НАН України 2019
Series:Математичні машини і системи
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Online Access:https://nasplib.isofts.kiev.ua/handle/123456789/162303
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Journal Title:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Cite this:Decrease of time of model synthesis in intellectual monitoring systems / A.S. Avramenko, S.V. Golub // Математичні машини і системи. — 2019. — № 3. — С. 129–134. — Бібліогр.: 8 назв. — англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
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Summary:The article investigates modern intellectual monitoring systems (IMS), which are able to predict the consequences of the adopted control decisions of decision support systems (DSS), thanks to the modeling of the characteristics of monitored objects. The drawbacks of existing implementations of IMS show when working in crisis monitoring. Since crisis monitoring imposes a number of restrictions on the speed of DSS and the high probability of failure of the trained IMS models, the use of existing implementations of IMS is problematic. The reasons of the existence of these shortcomings, and the algorithms with which it is connected lies in existing methodology. The paper investigates advantages and disadvantages of existing methods for the formation of inter-level relations in the IMS. A particular attention is paid to the method of classification of input data arrays (IDA) according to their characteristics, to the corresponding class of model synthesis algorithm (MSA). This paper proposes to improve the well-known method of classifying MIA by using unique adaptive classifiers for each of the MSA class.