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Название: Construction of Empirical Models of Complex Oscillation Processes with Non-Multiple Frequencies Based on the Principles of Genetic Algorithms
Другие названия: Побудова емпіричних моделей складних коливальних процесів з некратними частоти на принципах генетичних алгоритмів
Авторы: Горбійчук, Михайло
Біла, Ольга
Лазорів, Наталія
Horbiychuk, Mykhailo
Bila, Olha
Lazoriv, Nataliia
Принадлежность: Івано-Франківський національний технічний університет нафти і газу
Ivano-Frankivsk National Technical University of Oil and Gas
Библиографическое описание: Horbiychuk 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.
Bibliographic description: Horbiychuk 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.
Является частью издания: Energy engineering and control systems, 1 (5), 2019
Выпуск/№ : 1
Дата публикации: 26-фев-2019
Издательство: Lviv Politechnic Publishing House
Место издания, проведения: Львів
Ключевые слова: складний процес
емпірична модель
генетичний алгоритм
експеримент
програмне забезпечення
complex process
empirical model
unpredictable frequencies
genetic algorithm
Количество страниц: 10
Диапазон страниц: 29-38
Начальная страница: 29
Последняя страница: 38
Краткий осмотр (реферат): Розроблений 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.
A 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.
URI (Унифицированный идентификатор ресурса): http://ena.lp.edu.ua:8080/handle/ntb/45658
Владелец авторского права: © Національний університет “Львівська політехніка”, 2019
Список литературы: 1. Box J. Analysis of time series. Forecast and management / J. Box, G. Jenkins; transl. from the English .. – M.: Mir, 1974. – 406 p. (in Russian)
2. Ivakhnenko A. G. Long – term forecasting and management of complex systems / A. G.Ivakhnenko/ – K.: Tekhnika, 1975. – 312 p. (in Russian)
3. V. A. Uspenskii. Gödel's incompleteness theorem / V. A. Uspenskii. – Moscow: Nauka, 1982. – 110 p. (in Russian)
4. Ivakhnenko A. G. Modeling stability against disturbances / A. G. Ivakhnenko, V. S. Stepashko. – Kyiv: Nauk. Dumka, 1985. – 216 p. (in Russian)
5. Ivakhnenko A. G. The prediction of random processes / A. G. Ivakhnenko, V. G. Lapa. – Kyiv: Naukova Dumka, 1969. – 420 p. (in Russian)
6. Ivakhnenko A. G. Inductive method of self-organization of models of complex systems / A. G. Ivakhnenko. – Kyiv: Naukova Dumka, 1981. – 286 p. (in Russian)
7. Lemke F. Self-organising Modelling for Decision Support // International Conference in Inductive Modelling ICIM' 2013. Berlin, Germany. 172–178 pp.
8. Gorbiychuk M. I. Analysis of the parallel algorithm for synthesizing empirical models on the principles of genetic algorithms / M. I. Gorbichuk, V. M. Medvedchuk, A. N. Lazoriv // Problems of Control and Informatics. – 2016. – No. 1. – p. 112–130. (in Ukrainian)
9. Gorbiychuk M. I. Method of synthesis of empirical models based on the genetic algorithms / M. I. Gorbiychuk, M. I. Kogutyak, O. B. Vasilenko, І. V. Shchupak // Exploration and development of oil and gas fields. – 2009. – No. 4 (33). – p. 72–79. (in Ukrainian)
10] Gorbiychuk M. I. Synthesis of functions of classification on the basis of genetic algorithms / M. I. Gorbiychuk, S. T. Samulyak, І. В. Shchupak // Artificial Intelligence. – 2010. – No. 2. – p. 24–31. (in Ukrainian)
11. Moroz O. G. The GMDB algorithm with the genetic search for the optimal model / O. G. Moroz // Control systems and machines. – 2016. – No. 6. – p. 73–88. (in Russian)
12. Ivakhnenko A. G. Self-organization of predictive models / A. G. Ivakhnenko, J. A. Muller. – K.: Tehnika, 1984. – 223 p. (in Russian)
13. Verzhbitsky V. M. Fundamentals of numerical methods: a textbook for high schools / V. M.Verzhbitsky. – M .: Higher School, 2002. – 840 p. (in Russian)
14. Ivakhnenko A. G. Handbook on typical programs for modeling / A. G. Ivakhnenko, Yu. V. Koppa, V. S. Stepashko and others; Ed. A. G. Ivakhnenko. – К .: Technique, 1980. – 184 p. (in Russian)
15. 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)
16. 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)
References: 1. Box J. Analysis of time series. Forecast and management, J. Box, G. Jenkins; transl. from the English ., M., Mir, 1974, 406 p. (in Russian)
2. Ivakhnenko A. G. Long – term forecasting and management of complex systems, A. G.Ivakhnenko/ – K., Tekhnika, 1975, 312 p. (in Russian)
3. V. A. Uspenskii. Gödel's incompleteness theorem, V. A. Uspenskii, Moscow: Nauka, 1982, 110 p. (in Russian)
4. Ivakhnenko A. G. Modeling stability against disturbances, A. G. Ivakhnenko, V. S. Stepashko, Kyiv: Nauk. Dumka, 1985, 216 p. (in Russian)
5. Ivakhnenko A. G. The prediction of random processes, A. G. Ivakhnenko, V. G. Lapa, Kyiv: Naukova Dumka, 1969, 420 p. (in Russian)
6. Ivakhnenko A. G. Inductive method of self-organization of models of complex systems, A. G. Ivakhnenko, Kyiv: Naukova Dumka, 1981, 286 p. (in Russian)
7. Lemke F. Self-organising Modelling for Decision Support, International Conference in Inductive Modelling ICIM' 2013. Berlin, Germany. 172–178 pp.
8. Gorbiychuk M. I. Analysis of the parallel algorithm for synthesizing empirical models on the principles of genetic algorithms, M. I. Gorbichuk, V. M. Medvedchuk, A. N. Lazoriv, Problems of Control and Informatics, 2016, No. 1, p. 112–130. (in Ukrainian)
9. Gorbiychuk M. I. Method of synthesis of empirical models based on the genetic algorithms, M. I. Gorbiychuk, M. I. Kogutyak, O. B. Vasilenko, I. V. Shchupak, Exploration and development of oil and gas fields, 2009, No. 4 (33), p. 72–79. (in Ukrainian)
10] Gorbiychuk M. I. Synthesis of functions of classification on the basis of genetic algorithms, M. I. Gorbiychuk, S. T. Samulyak, I. V. Shchupak, Artificial Intelligence, 2010, No. 2, p. 24–31. (in Ukrainian)
11. Moroz O. G. The GMDB algorithm with the genetic search for the optimal model, O. G. Moroz, Control systems and machines, 2016, No. 6, p. 73–88. (in Russian)
12. Ivakhnenko A. G. Self-organization of predictive models, A. G. Ivakhnenko, J. A. Muller, K., Tehnika, 1984, 223 p. (in Russian)
13. Verzhbitsky V. M. Fundamentals of numerical methods: a textbook for high schools, V. M.Verzhbitsky, M ., Higher School, 2002, 840 p. (in Russian)
14. Ivakhnenko A. G. Handbook on typical programs for modeling, A. G. Ivakhnenko, Yu. V. Koppa, V. S. Stepashko and others; Ed. A. G. Ivakhnenko, K ., Technique, 1980, 184 p. (in Russian)
15. 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)
16. 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)
Тип содержания: Article
Располагается в коллекциях:Energy Engineering And Control Systems. – 2019. – Vol. 5, No. 1



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