Shamol generatori ish rejimini neyron tarmoqlar yordamida optimallashtirish

Shahboz Komilov

Article · 2023

Shamol generatori ish rejimini neyron tarmoqlar yordamida optimallashtirish

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20.1016/agroilm/2023/v12_i4/94089194Uzbek

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Shahboz Komilovno linked profile
TDTU

Abstract

Ushbu maqolada «Shamol generatori ish rejimini neyron tarmoqlar yordamida optimallashtirish» mavzusi tahlil qilingan. Tadqiqotda statistik tahlil va modellashtirish usullaridan foydalanilgan. Natijalar shamol generatori samaradorligini oshirishga xizmat qiladi.

Keywords

Bibliographic details

DRI: 20.1016/agroilm/2023/v12_i4/94089194
Work type: Article
Journal: Agro ilmISSN 2181-0575
Volume and issue: Vol. 12, No. 4
Pages: 1–6
Published: 1 August 2023
Language: Uzbek
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 16:49, version 1

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References

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

27 entries13 matched1 under review13 external

1.
Oripov I.U., Komilov Sh.G'., Aliyev U.K., va boshq. Shamol generatori tebranish xarakteristikalarini neyron tarmoqlar yordamida optimallashtirish // Texnika yulduzlari. – 2021. – №12. – B. 27–38. https://nationaldri.uz/20.1006/ty/2021/i12/20818585
MatchedExact by DRI→ Shamol generatori tebranish xarakteristikalarini neyron tarmoqlar yordamida optimallashtirish
2.
Umarov B.Z., Mamatova O.M. Paxta tozalsah mashinasi harorat rejimini genetik algoritm yordamida optimallashtirish // O'zDJTU ilmiy-uslubiy jurnali. – 2012. – №4. – B. 22–29. – DRI 20.1023/ozdjtuilm/2012/v2_i4/16667316
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Shukurova R.N., Normatova U.M., Abdurahmonov S.J. Quyosh kollektori ish rejimini matematik modellashtirish yordamida optimallashtrish // TDTU: fan, ta'lim va innovatsiya. – 2012. – №2. – B. 1–9. URL: https://nationaldri.uz/20.1006/tdtufti/2012/v2_i2/48648315
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Морозова Н.Г. Оптимизация температурного режима насосной станции методом математического моделирования // Физика твёрдого тела. – 2002. – № 12. – С. 49–107.
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Normatova S.I., Kenjayev B.F., Kamolova G.S., va boshq. Transformator asosiy podstansiyasi uchun kompozit materiallar asosida yangi konstruksiya ishlab chiqish // TDTU: fan, ta'lim va innovatsiya. – 2017. – №1. – B. 14–23.
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Schmidt P., Garcia L. (2003). Deep residual learning for image recognition. Sustainability, 111, 405–924.
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Волков А.Н. Оптимизация температурного режима асинхронного двигателя методом математического моделирования. – М.: Наука, 2020. – 202 с.
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Sharipov R.M. Kompressor qurilmasi uchun polimer qoplamalar asosida yangi konstruksiya ishlab chiqish // Xalq ta'limi. – 2020. – №4. – B. 77–91.
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Toshpo'latov U.O. Samarqandda qal'alri bo'yicha tarixiy-arxeologik tadqiqotlar (Namangan viloyati misolida) // «Tilshunoslik va adabiyotshunoslikning dolzarb masalalari» mavzusidagi respublika ilmiy-amaliy konferensiya materiallari. – Toshkent, 2019. – B. 33–34. https://nationaldri.uz/20.2002/mkfan2019/2019/62290621
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Kim S. Asinxron dvigatel ish rejimini eksperimental tadqiqot yordamida optimallashtirish. – Toshkent: Iqtisodiyot, 2019. – 130 b.
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Petrova A.S. Kompressor qurilmasi uchun polimer qoplamalar asosida yangi konstruksiya ishlab chiqish. – Toshkent: Adabiyot uchqunlari, 2010. – 192 b.
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Fayziyeva S.Z., Xamidova U.F. Nasos stansiyasi mustahkamlik ko'rsatkichlarini imitatsion modellashtirish yordamida optimallashtirish. – Toshkent: Innovatsiya, 2013. – 129 b.
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Лебедева О.М. Оптимизация температурного режима асинхронного двигателя методом математического моделирования. – М.: Наука, 2022. – 297 с.
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Garcia L. (2001). A meta-analysis of active learning in STEM education revisited. Computers & Education, 156, 212–904. DOI: 10.1016/j.compedu.2001.918150
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Sidorov V.P. Temir yo'l izi uchun kompozit materiallar asosida yangi konstruksiya ishlab chiqish. – Toshkent: Iqtisodiyot, 2022. – 215 b.
Under review
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Jabborov, L. G`., & Bozorova, M. S. (2014). On a Cauchy problem for a fractional differential equation. Matematika instituti byulleteni, 1(1), 15–27.
MatchedFuzzy match, S = 0.98
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Saidova, N. M. (2017). Biogaz manbalaridan foydalanib temir yo'l izi samaradorligini. Toshkent.
MatchedFuzzy match, S = 0.92
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To'xtayeva S.M., Eshmatov K.K. Quyosh kollektori ishonchliigini usullar bilan baholash // «Matematik fizika va tegishli muammolar» mavzusidagi respublika ilmiy-amaliy konferensiya materiallari. – Toshkent, 2020. – B. 3–4.
MatchedFuzzy match, S = 0.96
22.
Shukurova R.N., Komilov Sh.G'., Yusupova L.B., va boshq. Оптимизация режимов работы насосной танции методом математического моделирования (на примере Бухары) 44 // TDTU ilmiy-uslubiy jurnali. – 2020. – №4. – B. 1–4.
MatchedFuzzy match, S = 1.00
23.
Haydarova, O'. M. (2018). Nasos stansyasi ish rejimini eksperimental tadqiqot yordamida optimallashtirish. Texnika yulduzlari, 1–13. https://doi.org/10.52015/ty.2018.8.8.1
MatchedExact by DOI
24.
Garcia L., Wang L., Anderson R. (2018). Machine learning methods for credit scoring: a systematic review. Physical Review B, 141, 641–977. DOI: 10.1103/PhysRevB.2018.483406
External source
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O'zbekiston Respublikasi Vazirlar Mahkamasining 2019-yil 31-dekabrdagi 1059-son qarori. – Toshkent, 2019.
External source
26.
Shukurova R.N., Kamolova G.S. Biogaz manbalaridan foydalanib transformator podstansiyasi energiya samaradorligini oshirish // TDTU: fan, ta`lim va innovatsiya. – 2016. – №1. – B. 1–5.
MatchedFuzzy match, S = 1.00
27.
Asqarova, B. M., Komilova, N. F., & Eshonqulov, A. S. (2019). A comparative study of neologisms in Uzbek and English (case of Tashkent). O'zDJTU ilmiy-uslubiy jurnali, 9(4), 1–8. DOI: 10.52040/ozdjtuilm.2019.9.4.1
MatchedExact by DOI

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[1] 1.
Oripov I.U., Komilov Sh.G'., Aliyev U.K., va boshq. Shamol generatori tebranish xarakteristikalarini neyron tarmoqlar yordamida optimallashtirish // Texnika yulduzlari. – 2021. – №12. – B. 27–38. https://nationaldri.uz/20.1006/ty/2021/i12/20818585
MatchedExact by DRI→ Shamol generatori tebranish xarakteristikalarini neyron tarmoqlar yordamida optimallashtirish

[2] 2.
Umarov B.Z., Mamatova O.M. Paxta tozalsah mashinasi harorat rejimini genetik algoritm yordamida optimallashtirish // O'zDJTU ilmiy-uslubiy jurnali. – 2012. – №4. – B. 22–29. – DRI 20.1023/ozdjtuilm/2012/v2_i4/16667316
MatchedExact by DRI→ Paxta tozalash mashinasi harorat rejimini genetik algoritm yordamida optimallashtirish

[3] 3.
Samuelson P., Nordhaus W. Economics. 19th ed. – New York: McGraw-Hill, 2010. – 744 p.
External source

[4] 4.
Lee J., Schmidt P. (2010). Water use efficiency under deficit irrigation of cotton. Journal of Economic Perspectives, 81, 630–931.
External source

[5] 5.
Shukurova R.N., Normatova U.M., Abdurahmonov S.J. Quyosh kollektori ish rejimini matematik modellashtirish yordamida optimallashtrish // TDTU: fan, ta'lim va innovatsiya. – 2012. – №2. – B. 1–9. URL: https://nationaldri.uz/20.1006/tdtufti/2012/v2_i2/48648315
MatchedExact by DRI

[6] 6.
Морозова Н.Г. Оптимизация температурного режима насосной станции методом математического моделирования // Физика твёрдого тела. – 2002. – № 12. – С. 49–107.
External source

[7] 7.
Normatova S.I., Kenjayev B.F., Kamolova G.S., va boshq. Transformator asosiy podstansiyasi uchun kompozit materiallar asosida yangi konstruksiya ishlab chiqish // TDTU: fan, ta'lim va innovatsiya. – 2017. – №1. – B. 14–23.
MatchedFuzzy match, S = 1.00

[8] 8.
Schmidt P., Garcia L. (2003). Deep residual learning for image recognition. Sustainability, 111, 405–924.
External source

[9] 9.
Волков А.Н. Оптимизация температурного режима асинхронного двигателя методом математического моделирования. – М.: Наука, 2020. – 202 с.
External source

[10] 10.
Sharipov R.M. Kompressor qurilmasi uchun polimer qoplamalar asosida yangi konstruksiya ishlab chiqish // Xalq ta'limi. – 2020. – №4. – B. 77–91.
External source

[11] 11.
Toshpo'latov U.O. Samarqandda qal'alri bo'yicha tarixiy-arxeologik tadqiqotlar (Namangan viloyati misolida) // «Tilshunoslik va adabiyotshunoslikning dolzarb masalalari» mavzusidagi respublika ilmiy-amaliy konferensiya materiallari. – Toshkent, 2019. – B. 33–34. https://nationaldri.uz/20.2002/mkfan2019/2019/62290621
MatchedExact by DRI

[12] 12.
Kim S. Asinxron dvigatel ish rejimini eksperimental tadqiqot yordamida optimallashtirish. – Toshkent: Iqtisodiyot, 2019. – 130 b.
External source

[13] 13.
Petrova A.S. Kompressor qurilmasi uchun polimer qoplamalar asosida yangi konstruksiya ishlab chiqish. – Toshkent: Adabiyot uchqunlari, 2010. – 192 b.
External source

[14] 14.
Fayziyeva S.Z., Xamidova U.F. Nasos stansiyasi mustahkamlik ko'rsatkichlarini imitatsion modellashtirish yordamida optimallashtirish. – Toshkent: Innovatsiya, 2013. – 129 b.
MatchedFuzzy match, S = 1.00

[15] 15.
Лебедева О.М. Оптимизация температурного режима асинхронного двигателя методом математического моделирования. – М.: Наука, 2022. – 297 с.
External source

[16] 16.
Выготский Л.С. Педагогическая психология. – М.: Педагогика, 1991. – 480 с.
External source

[17] 17.
Garcia L. (2001). A meta-analysis of active learning in STEM education revisited. Computers & Education, 156, 212–904. DOI: 10.1016/j.compedu.2001.918150
External source

[18] 18.
Sidorov V.P. Temir yo'l izi uchun kompozit materiallar asosida yangi konstruksiya ishlab chiqish. – Toshkent: Iqtisodiyot, 2022. – 215 b.
Under review

[19] 19.
Jabborov, L. G`., & Bozorova, M. S. (2014). On a Cauchy problem for a fractional differential equation. Matematika instituti byulleteni, 1(1), 15–27.
MatchedFuzzy match, S = 0.98

[20] 20.
Saidova, N. M. (2017). Biogaz manbalaridan foydalanib temir yo'l izi samaradorligini. Toshkent.
MatchedFuzzy match, S = 0.92

[21] 21.
To'xtayeva S.M., Eshmatov K.K. Quyosh kollektori ishonchliigini usullar bilan baholash // «Matematik fizika va tegishli muammolar» mavzusidagi respublika ilmiy-amaliy konferensiya materiallari. – Toshkent, 2020. – B. 3–4.
MatchedFuzzy match, S = 0.96

[22] 22.
Shukurova R.N., Komilov Sh.G'., Yusupova L.B., va boshq. Оптимизация режимов работы насосной танции методом математического моделирования (на примере Бухары) 44 // TDTU ilmiy-uslubiy jurnali. – 2020. – №4. – B. 1–4.
MatchedFuzzy match, S = 1.00

[23] 23.
Haydarova, O'. M. (2018). Nasos stansyasi ish rejimini eksperimental tadqiqot yordamida optimallashtirish. Texnika yulduzlari, 1–13. https://doi.org/10.52015/ty.2018.8.8.1
MatchedExact by DOI

[24] 24.
Garcia L., Wang L., Anderson R. (2018). Machine learning methods for credit scoring: a systematic review. Physical Review B, 141, 641–977. DOI: 10.1103/PhysRevB.2018.483406
External source

[25] 25.
O'zbekiston Respublikasi Vazirlar Mahkamasining 2019-yil 31-dekabrdagi 1059-son qarori. – Toshkent, 2019.
External source

[26] 26.
Shukurova R.N., Kamolova G.S. Biogaz manbalaridan foydalanib transformator podstansiyasi energiya samaradorligini oshirish // TDTU: fan, ta`lim va innovatsiya. – 2016. – №1. – B. 1–5.
MatchedFuzzy match, S = 1.00

[27] 27.
Asqarova, B. M., Komilova, N. F., & Eshonqulov, A. S. (2019). A comparative study of neologisms in Uzbek and English (case of Tashkent). O'zDJTU ilmiy-uslubiy jurnali, 9(4), 1–8. DOI: 10.52040/ozdjtuilm.2019.9.4.1
MatchedExact by DOI