Hozirgi kunda ishlab chiqarish tizimlarida energiya resurslarini
samarali ishlatish, ishlab chiqarish jarayonlarini optimallashtirish va tizimlarning
noaniqlik va o‘zgaruvchanlik sharoitida moslashuvchanligini ta’minlash muhim
ahamiyatga ega. Ishlab chiqarish liniyalari va texnik tizimlar ko‘p holatlarda
tasodifiy ta’sirlarga uchraydi, bu esa ularning samaradorligi va mahsulot sifatini
pasaytirishi mumkin. Shuningdek, tizimning tez va samarali qaror qabul qilish
qobiliyati, resurslarni optimallashtirish, vaqti-vaqti bilan yuzaga keladigan
o‘zgarishlarga moslashish zarurati mavjud. Ushbu muammolarni yechish
uchun texnik kognitiv tizimlar asosida kvant genetik algoritmlar yordamida
moslashuvchan boshqaruv modeli taklif etildi. Kvant hisoblash va genetik
algoritmlar yordamida tizimlar optimallashtiriladi, bu esa ishlab chiqarish
jarayonlari samaradorligini oshiradi. Model ko‘p o‘lchamli tizimlarda energiya
resurslarini tejash va qaror qabul qilish jarayonini tezlashtirishga yordam beradi.
Natijada ishlab chiqarish tizimlari yanada samarali va moslashuvchan bo‘lib,
mahsulot sifatini yaxshilash hamda resurslardan maksimal darajada foydalanish
imkonini beradi.
Hozirgi kunda ishlab chiqarish tizimlarida energiya resurslarini
samarali ishlatish, ishlab chiqarish jarayonlarini optimallashtirish va tizimlarning
noaniqlik va o‘zgaruvchanlik sharoitida moslashuvchanligini ta’minlash muhim
ahamiyatga ega. Ishlab chiqarish liniyalari va texnik tizimlar ko‘p holatlarda
tasodifiy ta’sirlarga uchraydi, bu esa ularning samaradorligi va mahsulot sifatini
pasaytirishi mumkin. Shuningdek, tizimning tez va samarali qaror qabul qilish
qobiliyati, resurslarni optimallashtirish, vaqti-vaqti bilan yuzaga keladigan
o‘zgarishlarga moslashish zarurati mavjud. Ushbu muammolarni yechish
uchun texnik kognitiv tizimlar asosida kvant genetik algoritmlar yordamida
moslashuvchan boshqaruv modeli taklif etildi. Kvant hisoblash va genetik
algoritmlar yordamida tizimlar optimallashtiriladi, bu esa ishlab chiqarish
jarayonlari samaradorligini oshiradi. Model ko‘p o‘lchamli tizimlarda energiya
resurslarini tejash va qaror qabul qilish jarayonini tezlashtirishga yordam beradi.
Natijada ishlab chiqarish tizimlari yanada samarali va moslashuvchan bo‘lib,
mahsulot sifatini yaxshilash hamda resurslardan maksimal darajada foydalanish
imkonini beradi.
В современных производственных системах особое значение
приобретают эффективное использование энергетических ресурсов,
оптимизация производственных процессов и обеспечение адаптивности
систем в условиях неопределённости и изменчивости. Производственные
линии и технические системы зачастую подвержены случайным
воздействиям, что может снижать их эффективность и качество
продукции. Также существует необходимость в способности системы
быстро и эффективно принимать решения, оптимизировать ресурсы и
адаптироваться к возникающим изменениям. Для решения этих задач
предложена модель адаптивного управления, основанная на технических
когнитивных системах и квантовых генетических алгоритмах. С исполь-
зованием квантовых вычислений и генетических алгоритмов осуществля-
ется оптимизация систем, что повышает эффективность производствен-
ных процессов. Разработанная модель способствует экономии энерге-
тических ресурсов в многомерных системах и ускоряет процесс принятия
решений. В результате производственные системы становятся более
эффективными и адаптивными, что позволяет повысить качество продук-
ции и максимально эффективно использовать ресурсы.
Nowadays, it is important to efficiently use energy resources in
production systems, optimize production processes, and ensure system flexibility
in the face of uncertainty and variability. Production lines and technical systems
are often subject to random influences that can reduce their efficiency and product
quality. It is also necessary for the system to be able to make fast and effective
decisions, optimize resources, and adapt to changes that occur from time to time.
We propose an adaptive control model based on technical cognitive systems,
which utilizes quantum genetic algorithms to address these issues. With the help
of quantum computing and genetic algorithms, systems are optimized, which
increases the efficiency of production processes. The model helps save energy
resources and accelerate the decision-making process in multidimensional systems.
As a result, production systems become more efficient and flexible, which improves
product quality and maximizes resource use.
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Yakubova N.S. | dotsent | Toshkent davlat texnika universiteti |
| № | Name of reference |
|---|---|
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| 7 | Popov, I. Yu. (2007). Kvantovyy kompyuter i kvantovye algoritmy [Quantum computer and quantum algorithms]. Saint Petersburg: SPbGU ITMO |
| 8 | Shill, P. C., Amin, F., & Murase, K. (2011). Parameter optimization based on quantum genetic algorithms for fuzzy logic controller. In Proceedings of the 27th Fuzzy System Symposium (pp. 1065– 1068). Japan. |
| 9 | Shill, P. C., Sarker, B., Urmi, M. C., & Murase, K. (2012). Quantum fuzzy controller for inverted pendulum system based on quantum genetic optimization. International Journal of Advanced Research in Computer Science, 3(7), 1–8. |
| 10 | Stieb, W. H., & Hardy, J. (2007). Zadachi i ikh resheniya v kvantovykh vychisleniyakh i kvantovoy teorii informatsii [Problems and their solutions in quantum computing and quantum information theory]. Izhevsk: Institute of Computer Research. |
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| 12 | Ulyanov, S. V. (2014b). Self-organizing quantum robust control methods and systems for situations with uncertainty and risk. U.S. Patent No. 8,788,450 B2. |
| 13 | Ulyanov, S. V., Mishin, A. A., & Minogin, A. A. (2010). Informatsionnaya tekhnologiya proektirovaniya robustnykh baz znaniy nechetkikh regulyatorov. Chastʹ III: Kvantovyy nechetkij vyvod i kvantovaya informatsiya [Information technology for robust fuzzy knowledge base design. Part III: Quantum fuzzy inference and quantum information]. Sistemnyy analiz v nauke i obrazovanii – Systems Analysis in Science and Education, (3). |
| 14 | Usmanov, K. I., Yakubova, N. S., Urmanova, V. T., & Abdurasulova, G. E. (2023a). Synthesis of a control system for the process of diesel fuel hydropuri�ication with the Adar method. E3S Web of Conferences, 458, 01025. https://doi.org/10.1051/e3sconf/202345801025 |
| 15 | Usmanov, K., Yakubova, N. S., & Eshbobaev, J. (2023b). Modeling and optimization of the ammonium solution extraction process. Engineering Proceedings, 56, 198. https://doi.org/10.3390/ ASEC2023-16274 |