КҮШЕЙТУ АРҚЫЛЫ ОҚЫТУДЫ ПАЙДАЛАНА ОТЫРЫП, ЗИЯТКЕРЛІК ӨНДІРІСТІ БАСҚАРУ ӘДІСТЕРІ
https://doi.org/10.55452/1998-6688-2026-23-2-187-204
Аңдатпа
Бүгінде ақылды өндіріс жүйелерін құру – өзекті міндет. Күрделі өндірістік мәселелерді шешуге мүмкіндік беретін нейрондық желілер кеңінен қолданылады. Мақала GEMMA моделінің бөлігі ретінде өнеркәсіптік жабдықтардың күйін анықтауға арналған DQN, PPO сияқты нейрондық желілер негізінде құрылған арматурамен оқыту алгоритмдеріне арналған. Француздық GEMMA моделі SFC (Sequential Function Charts) тіліне негізделген және процестерді басқару стандарттарын қамтиды. GEMMA моделінің D аймағына нейрондық желілерді енгізу ұсынылады. Модельдеу және эксперимент нәтижелері екі түрлі деректер базасы негізінде жүзеге асырылды: біреуі жасанды түрде жасалды, екіншісі өнеркәсіптік өндірістен алынды. Қарастырылған архитектураларды қолдану өнеркәсіптік деректермен жұмыс істеу үшін жақсы нәтижелерге қол жеткізуге мүмкіндік береді.
Авторлар туралы
З. И СамигулинаҚазақстан
PhD.
Алматы қ.
Б. Ж. Дюсенкулова
Қазақстан
Докторант.
Алматы қ.
Д. А. Бутакова
Қазақстан
Магистрант.
Алматы қ.
Әдебиет тізімі
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Рецензия
Дәйектеу үшін:
Самигулина З.И., Дюсенкулова Б.Ж., Бутакова Д.А. КҮШЕЙТУ АРҚЫЛЫ ОҚЫТУДЫ ПАЙДАЛАНА ОТЫРЫП, ЗИЯТКЕРЛІК ӨНДІРІСТІ БАСҚАРУ ӘДІСТЕРІ. Қазақстан-Британ техникалық университетінің хабаршысы. 2026;23(2):187-204. https://doi.org/10.55452/1998-6688-2026-23-2-187-204
For citation:
Samigulina Z.I., Dyussenkulova B.Z., Butakova D.A. REINFORCEMENT LEARNING–DRIVEN CONTROL STRATEGIES FOR SMART MANUFACTURING SYSTEMS. Herald of the Kazakh-British Technical University. 2026;23(2):187-204. https://doi.org/10.55452/1998-6688-2026-23-2-187-204
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