Construction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms

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dc.citation.epage38
dc.citation.issue1
dc.citation.spage29
dc.contributor.affiliationІвано-Франківський національний технічний університет нафти і газу
dc.contributor.affiliationIvano-Frankivsk National Technical University of Oil and Gas
dc.contributor.authorГорбійчук, Михайло
dc.contributor.authorБіла, Ольга
dc.contributor.authorЛазорів, Наталія
dc.contributor.authorHorbiychuk, Mykhailo
dc.contributor.authorBila, Olha
dc.contributor.authorLazoriv, Nataliia
dc.coverage.placenameЛьвів
dc.date.accessioned2020-02-18T10:50:56Z
dc.date.available2020-02-18T10:50:56Z
dc.date.created2019-02-26
dc.date.issued2019-02-26
dc.description.abstractРозроблений метод побудови емпіричних моделей складних процесів на основі генетичних алгоритмів, що дозволяє, порівняно з індуктивним методом самоорганізації моделей, значно скоротити витрати машинного часу на їх реалізацію. Використаний підхід, що дозволяє складну модель розглядати як композицію трьох складових – лінійного тренда, коливальної складової з некратними частотами і рівняння регресії, що спрощує процес побудови складних моделей. Для реалізації запропонованого методу розроблено алгоритмічне і програмне забезпечення. На конкретному прикладі залежності рівня води в р. Дністер від погодних умов показано, що модель, побудована на основі запропонованого методу, з достатньою точністю описує поведінку складних процесів. Отримана емпірична модель може бути використана для прогнозування рівня води залежно від погодних умов.
dc.description.abstractA method for constructing the empirical models of complex processes has been developed on the basis of genetic algorithms which, compared to the inductive method of self-organization of models, significantly reduces computer time for their implementation. An approach has been used that allows a complex model to be considered as a composition of three components, i.e. a linear trend, an oscillatory component with non-multiple frequencies and a regression equation which simplifies the process of building complex models. To implement the proposed method, algorithms and software have been developed based on a specific example of the dependence of the water level in the river. The Dniester River weather conditions show that a model built on the basis of the proposed method describes the behavior of complex processes with sufficient accuracy. The resulting empirical model can be used to predict the water level depending on weather conditions.
dc.format.extent29-38
dc.format.pages10
dc.identifier.citationHorbiychuk M. Construction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms / Mykhailo Horbiychuk, Olha Bila, Nataliia Lazoriv // Energy engineering and control systems. — Львів : Lviv Politechnic Publishing House, 2019. — Vol 5. — No 1. — P. 29–38.
dc.identifier.citationenHorbiychuk M. Construction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms / Mykhailo Horbiychuk, Olha Bila, Nataliia Lazoriv // Energy engineering and control systems. — Lviv Politechnic Publishing House, 2019. — Vol 5. — No 1. — P. 29–38.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45658
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofEnergy engineering and control systems, 1 (5), 2019
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dc.relation.referencesen12. Ivakhnenko A. G. Self-organization of predictive models, A. G. Ivakhnenko, J. A. Muller, K., Tehnika, 1984, 223 p. (in Russian)
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dc.relation.referencesen15. Gorbiychuk M. I. Method of motivating mathematical models of folding processes based on the genetic algorithms, M. I. Gorbiychuk, M. A. Shufnarovich, Information Problems of Computer Systems, Jurisprudence, Energy, Economy, Modeling and Control: Proceedings of the International Conference on Science and Technology, Buchach, 01-04.06, 2010, pp. 328–332. (in Ukrainian)
dc.relation.referencesen16. Rutkovskaya D. Neural networks, genetic algorithms and fuzzy systems, D. Rutkovskaya, M. Pilinsky, L. Rutkovsky; trans. from Polish. I. D. Rudinsky, Moscow: Hot line-Telecom, 2004, 452 p. (in Russian)
dc.rights.holder© Національний університет “Львівська політехніка”, 2019
dc.subjectскладний процес
dc.subjectемпірична модель
dc.subjectгенетичний алгоритм
dc.subjectексперимент
dc.subjectпрограмне забезпечення
dc.subjectcomplex process
dc.subjectempirical model
dc.subjectunpredictable frequencies
dc.subjectgenetic algorithm
dc.titleConstruction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms
dc.title.alternativeПобудова емпіричних моделей складних коливальних процесів з некратними частоти на принципах генетичних алгоритмів
dc.typeArticle

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Energy Engineering and Control Systems. – 2019. – Vol. 5, No. 1