Development of object state estimation method in intelligent decision support systems

A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. Th...

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Autores principales:	Vitalii Bezuhlyi, Volodymyr Oliynyk, Іgor Romanenko, Oleksandr Zhuk, Vasyl Kuzavkov, Oleh Borysov, Serhii Korobchenko, Eduard Ostapchuk, Taras Davydenko, Andrii Shyshatskyi
Formato:	article
Lenguaje:	EN RU UK
Publicado:	PC Technology Center 2021
Materias:	decision support systems artificial neural networks genetic algorithm Technology (General) T1-995 Industry HD2321-4730.9
Acceso en línea:	https://doaj.org/article/1d9577826f4e48508024501b9ed5a393
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id	oai:doaj.org-article:1d9577826f4e48508024501b9ed5a393
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spelling	oai:doaj.org-article:1d9577826f4e48508024501b9ed5a3932021-11-04T14:06:30ZDevelopment of object state estimation method in intelligent decision support systems1729-37741729-406110.15587/1729-4061.2021.239854https://doaj.org/article/1d9577826f4e48508024501b9ed5a3932021-10-01T00:00:00Zhttp://journals.uran.ua/eejet/article/view/239854https://doaj.org/toc/1729-3774https://doaj.org/toc/1729-4061A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. The advanced genetic algorithm is used when constructing a fuzzy cognitive model and increases the efficiency of identifying factors and relationships between them by simultaneously finding a solution by several individuals. The objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The method also contains an improved procedure for processing initial data under a priori uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method increases the efficiency of data processing at the level of 11–15 % using additional advanced procedures. The proposed method can be used in DSS of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, as well as in DSS for logistics ACS of the Armed ForcesVitalii BezuhlyiVolodymyr OliynykІgor RomanenkoOleksandr ZhukVasyl KuzavkovOleh BorysovSerhii KorobchenkoEduard OstapchukTaras DavydenkoAndrii ShyshatskyiPC Technology Centerarticledecision support systemsartificial neural networksgenetic algorithmTechnology (General)T1-995IndustryHD2321-4730.9ENRUUKEastern-European Journal of Enterprise Technologies, Vol 5, Iss 3 (113), Pp 54-64 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	decision support systems artificial neural networks genetic algorithm Technology (General) T1-995 Industry HD2321-4730.9
spellingShingle	decision support systems artificial neural networks genetic algorithm Technology (General) T1-995 Industry HD2321-4730.9 Vitalii Bezuhlyi Volodymyr Oliynyk Іgor Romanenko Oleksandr Zhuk Vasyl Kuzavkov Oleh Borysov Serhii Korobchenko Eduard Ostapchuk Taras Davydenko Andrii Shyshatskyi Development of object state estimation method in intelligent decision support systems
description	A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. The advanced genetic algorithm is used when constructing a fuzzy cognitive model and increases the efficiency of identifying factors and relationships between them by simultaneously finding a solution by several individuals. The objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The method also contains an improved procedure for processing initial data under a priori uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method increases the efficiency of data processing at the level of 11–15 % using additional advanced procedures. The proposed method can be used in DSS of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, as well as in DSS for logistics ACS of the Armed Forces
format	article
author	Vitalii Bezuhlyi Volodymyr Oliynyk Іgor Romanenko Oleksandr Zhuk Vasyl Kuzavkov Oleh Borysov Serhii Korobchenko Eduard Ostapchuk Taras Davydenko Andrii Shyshatskyi
author_facet	Vitalii Bezuhlyi Volodymyr Oliynyk Іgor Romanenko Oleksandr Zhuk Vasyl Kuzavkov Oleh Borysov Serhii Korobchenko Eduard Ostapchuk Taras Davydenko Andrii Shyshatskyi
author_sort	Vitalii Bezuhlyi
title	Development of object state estimation method in intelligent decision support systems
title_short	Development of object state estimation method in intelligent decision support systems
title_full	Development of object state estimation method in intelligent decision support systems
title_fullStr	Development of object state estimation method in intelligent decision support systems
title_full_unstemmed	Development of object state estimation method in intelligent decision support systems
title_sort	development of object state estimation method in intelligent decision support systems
publisher	PC Technology Center
publishDate	2021
url	https://doaj.org/article/1d9577826f4e48508024501b9ed5a393
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Development of object state estimation method in intelligent decision support systems

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