Optimization of pumping stations using neural networks (case of Bukhara)

B. Umarov; Yoqubov Umar Jamshid o'g'li; Fayziyev Yusuf Jasur o'g'li

Article · 2021

Optimization of pumping stations using neural networks (case of Bukhara)

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20.1011/terdu/2021/v13_i1/36807365English

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Authors

B. Umarov ORCID 0000-0002-0028-0183 (opens in a new tab)
TUACE
Yoqubov Umar Jamshid o'g'lino linked profile
Toshkent arxitektura-qurilish universiteti
Fayziyev Yusuf Jasur o'g'li ORCID 0000-0002-0028-060X (opens in a new tab)
Tashkent University of Architecture and Civil Engineering

Abstract

The article examines optimization of pumping stations using neural networks (case of Bukhara). A comparative analysis of international experience is provided. Practical recommendations are proposed.

Keywords

Bibliographic details

DRI: 20.1011/terdu/2021/v13_i1/36807365
Work type: Article
Journal: TerDU ilmiy axborotnomasiISSN 2181-0451
Volume and issue: Vol. 13, No. 1
Pages: 9–15
Published: 29 January 2021
Language: English
Subject: Arxitektura
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 16:48, version 1

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References

Sources cited by this work and how they were matched to works in the registry.

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[1] 1.
Kuznetsova Y.A. Elektr tarmog'i ishonchliligini sonli usullar bilan baholash. – Toshkent: Iqtisodiyot, 2006. – 237 b.
External source

[2] 2.
Петрова А.С. Оптимизация температурного режима асинхронного двигателя методом математического моделирования. – М.: Наука, 1989. – 397 с.
External source

[3] 3.
Ikromova S.G'., Mansurova Y.S., Xamidov H.M. Qumli tuproqlarda sholi yetishtirishning ilmiy asoslari // TIQXMMI ilmiy-uslubiy jurnali. – 2016. – №2. – B. 1–12. URL: https://nationaldri.uz/20.1027/tiqxmmiilm/2016/v8_i2/69932927
MatchedExact by DRI

[4] 4.
Morozova N.G. G'altakli konveyer uchun bazalt tolasi asosida yangi konstruksiya ishlab chiqish // Til va adabiyot ta'limi. – 2000. – №7. – B. 15–75.
External source

[5] 5.
Umarov B.Z. Shamol generatori uchun bazalt tolasi asosida yangi konstruksiya ishlab chiqish: fan nomzodi (PhD) diss. avtoreferati. – Toshkent, 2019. – 144 b.
Under review

[6] 6.
Rossi F., Silva R., Kumar V. (1999). Deep residual learning for image recognition revisited. Agricultural Water Management, 4, 684–968.
External source

[7] 7.
Garcia L. (2013). Deep residual learning for image recognition. The Lancet, 125, 284–923. https://doi.org/10.1016/S0140-6736.2013.873653
External source

[8] 8.
Ushinskiy K.D. Tanlangan pedagogik asarlar. – Toshkent: O'qituvchi, 1968. – 380 b.
External source

[9] 9.
Qurbonova Sh.S. Geotermal energiya manbalaridan foydalanib transformator podstansiyasi energiya samaradorligini oshirish. – Toshkent: Fan, 2013. – 221 b.
Under review

[10] 10.
Nazarova, Z. I., & Moʼminova, O. Gʼ. (2017). Nasos stansiyasi texnologik parametrlarini eksperimental tadqiqot yordamida optimallashtirish. In «Agrar sohada resurstejamkor texnologiyalar» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 22–23). Toshkent.
MatchedFuzzy match, S = 0.93

[11] 11.
Müller K., Kumar V., Smith J. (2019). Critical thinking assessment in university students: a systematic review. Agricultural Water Management, 83, 466–905.
External source

[12] 12.
Yoqubova X.S., Kuznetsova Y.A. Sensor ma'lumotlarini fuzzy logika yoramida tahlil qilish // Raqamli texnologiyalar va ta'lim. – 2014. – №4. – B. 1–7. – DRI 20.2002/rtt/2014/v1_i4/13775167
MatchedExact by DRI

[13] 13.
Nguyen T., Smith J. (1999). Critical thinking assessment in university students. Sustainability, 101, 179–901.
External source

[14] 14.
Икромов Ш.Т. Тошкент воҳасида ёдгорликлари бўйича тарихий-археологик тадқиқотлар // ТошДАУ илмий-услубий журнали. – 2012. – №3. – Б. 13–24.
MatchedFuzzy match, S = 1.00

[15] 15.
Ro'ziyeva Sh.U. Issiqlik almashtirgich energiya sarfini genetik algoritm yordamida optimallashtirish. – Toshkent: Iqtisodiyot, 2014. – 291 b.
External source