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It is known that the importance of the modeling method in the research of technological systems is increasing. One of the main reasons for this can be attributed to the possibility of simplifying a complex system, as well as the priority aspects of logical consistency and mathematical laws. At the same time, the development of information technology, computing and artificial intelligence has strengthened the practice of using modeling methods. In other words, the modeling apparatus has a good integration feature with these concepts. In this article, the importance and relevance of the development of optimal control models based on a neural network is presented as the main scientific idea on the example of a closed-loop control system. When the control system is formed as an intelligent system, or when the system description is based on the concepts of an intelligent system, the main parameters of modeling are differentiated. It is theoretically and practically based that the results of modeling gain significance depending on these parameters. The effectiveness of the use of neural network models in the optimal control of the activity of the intelligent system is explained against the background of the possibility of minimizing the control error. It has been scientifically proven that modeling closed systems using neural networks has several advantages. Quantitative parameters that need to be paid attention to in the formation of an intelligent system are researched. Conclusions and proposals are presented on the effectiveness of using neural networks in modeling complex technological systems.

  • Web Address
  • DOI
  • Date of creation in the UzSCI system 19-05-2024
  • Read count 167
  • Date of publication 15-09-2023
  • Main LanguageIngliz
  • Pages17-26
English

It is known that the importance of the modeling method in the research of technological systems is increasing. One of the main reasons for this can be attributed to the possibility of simplifying a complex system, as well as the priority aspects of logical consistency and mathematical laws. At the same time, the development of information technology, computing and artificial intelligence has strengthened the practice of using modeling methods. In other words, the modeling apparatus has a good integration feature with these concepts. In this article, the importance and relevance of the development of optimal control models based on a neural network is presented as the main scientific idea on the example of a closed-loop control system. When the control system is formed as an intelligent system, or when the system description is based on the concepts of an intelligent system, the main parameters of modeling are differentiated. It is theoretically and practically based that the results of modeling gain significance depending on these parameters. The effectiveness of the use of neural network models in the optimal control of the activity of the intelligent system is explained against the background of the possibility of minimizing the control error. It has been scientifically proven that modeling closed systems using neural networks has several advantages. Quantitative parameters that need to be paid attention to in the formation of an intelligent system are researched. Conclusions and proposals are presented on the effectiveness of using neural networks in modeling complex technological systems.

№ Name of reference
1 [1] Методы робастного, нейронечёткого и адаптивного управления. Учебник. Под ред. Егупова Н.Д., 2е изд. –М: Изд-во МГТУ им. Баумана, 2002.
2 [2] Бошляков А.А., Рубцов В.И. Проектирование нечеткого регулятора следящей системы. Инженерный журнал: наука и инновации, 2013, вып.8.
3 [3] Yusupbekov N.R. va boshqalar. Boshqarishning intellektual tizimlari va qaror qabul qilish. “O‘zbekiston milliy ensiklopediyasi” davlat ilmiy nashriyoti. Toshkent – 2015.
4 [4] Данилова М.Г. и друг. Моделирование системы прямого управления моментом асинхронного двигвтеля с регулятором на основе нечеткой логики в Simulink. Инженeрный вестник Дона, №2. 2017 г.
5 [5] Алиев Р.А., Алиев Р.Р. Теория интеллектуaльных систем и ее применение. –Баку: Чашыоглы, 2001.
6 [6] Рустовская Д. и друг. Нейронные сети, генетические алгоритмы и нечёткие системы. – М: Горчая линия – Телеком, 2006
7 [7] Михайленко В.С., Харченко Р.Ю. Адаптивная настройка нечеткого ПИ – регулятора по идентификации переходного процесса. Працы Одеського политехнического университету. 2012. Вып. 1(38)
8 [8] Кудрявцев В.С. Применение нечетких лингвистических регуляторов для управления сложными динамическими объектами. Диссертация на соискание ученой степени кандидата технических наук. Екатеринбург -2003.
9 [9] Е.В.Лубенцова и друг. Метод построения нечетких регуляторов с использованием аналитических выражений для управляющих воздействий. Fundamental research, №11, 2015.
10 [10] Чернецкая И.В. Нечеткие регуляторы в системах автоматического регулирования. Вестник ЮУрГУ, №14, 2006
11 [11] Куленко М.С. Буренин С.В. Исследование применения нечетких регуляторов в системах управления технологическим процессами. Вестник ИГЭУ, Вып.2, 2010
12 [12] Jo‘rayev F.D. Qishloq xo‘jalik mahsulotlari ishlab chiqarishni qisqa muddatli prognozlashtirish. Innovatsion texnologiyalar. №2 (42). – 2021. – 92-95 б. https://cyberleninka.ru/article/n/ishlo-h-zhalik-ma-sulotlari-ishlab-chi-arishni-is-a-muddatliprognozlashtirish/viewer.
13 [13] Солонников Ю.Я., Иванов В.Э. Применение алгоритмов нечеткой логики на основе ПЛК SIEMENSS7-300 для системы управления электропривода. Ученые заметки ТОГУ, Том 8, №3, 2017.
14 [14] Д.А.Телеченко. Современные системы управления. Сборник учебно-методических материалов. Благовещенск: Амурский гос. ун-т, 2016.
15 [15] Демелова Г.Л. и др. Особенности применения нечетких регуляторов на примере управления скоростью вращения электродвигателя постоянного тока. Научно – технический вестник информационных технологий, механики и оптики, Том 16, №5, 2016.
16 [16] Жураев, Ф. (2021). Перспективные проблемы развития производства сельскохозяйственной продукции и их эконометрическое моделирование. Экономика и образование, (4), 377–385. // https://cedr.tsue.uz/index.php/journal/article/view/190.
17 [17] Jurayev F.D. Econometric modeling of the development and management of agricultural production based on cluster analysis (on the example of the Kashkadarya region) // Экономика и предпринимательство, (ISSN 1999-2300), Vol. 15, № 8 (133) 2021. с:584–590 // DOI: 10.34925/EIP.2021.133.8.112 / https://www.elibrary.ru/item.asp?id=47173980.
18 [18] Ochilov, M. A., Juraev, F. D., Maxmatqulov, G. X., & Rahimov, A. M. (2020). Analysis of important factors in checking the optimality of an indeterminate adjuster in a closed system. Journal of Critical Review, 7(15), 1679-1684.
19 [19] Jo‘rayev, F. D. S., & Ochilov, M. A. (2023). Algorithms for multi-factory polynomial modeling of technological processes. Chemical Technology, Control and Management, 2023(1), 59-67. / https://ijctcm.researchcommons.org/journal/vol2023/iss1/8/.
20 [20] Jo‘rayev, F. D. Econometric modeling of the development and management of agricultural production based on cluster analysis (on the example of the584 Kashkadarya region). экономика, (8), 584-590.
21 [21] Маллаев, А. Р., & Жураев, Ф. Д. (2017). Операционная теория исчисления по преобразованию лапласа. Научное знание современности, (7), 5-16.
22 [22] Rakhimov, A. N., & Jo’rayev, F. D. (2022). A Systematic Approach To The Methodology Of Agricultural Development And The Strategy Of Econometric Modeling. resmilitaris, 12(4), 2164-2174. https://resmilitaris.net/menu-script/index.php/resmilitaris/article/view/2060.
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