NEYRON TARMOQLAR NAZARIYASINING SHAKLLANISHI VA IQTISODIY BASHORATLASHDAGI ILMIY ASOSLARI
Keywords:
Kalit so‘zlar: sun’iy neyron tarmoqlar, Deep Learning, iqtisodiy bashoratlash, sun’iy intellekt, Big Data, LSTM, RNN, iqtisodiy modellashtirish, prognozlash, mashinali o‘rganishAbstract
Annotatsiya: Mazkur maqolada neyron tarmoqlar nazariyasining shakllanish va rivojlanish bosqichlari, ularning iqtisodiy bashoratlash jarayonlaridagi ilmiy-uslubiy ahamiyati tahlil qilingan. Sun’iy neyron tarmoqlarning biologik asoslari, dastlabki matematik modellari hamda Deep Learning texnologiyalarigacha bo‘lgan evolyutsiyasi yoritilgan. Shuningdek, xorijiy, MDH va O‘zbekiston olimlarining iqtisodiy prognozlash sohasida neyron tarmoqlardan foydalanish bo‘yicha ilmiy tadqiqotlari o‘rganilgan. Tadqiqot natijalariga ko‘ra, neyron tarmoqlar murakkab va chiziqli bo‘lmagan iqtisodiy bog‘liqliklarni aniqlash, katta hajmdagi ma’lumotlarni qayta ishlash hamda yuqori aniqlikdagi prognozlarni shakllantirishda samarali vosita hisoblanadi.
References
1. Stanford University. AI Index Report 2022. – Stanford Institute for Human-Centered Artificial Intelligence (HAI), 2022.
2. McKinsey & Company. The State of AI Report 2022. – McKinsey Global Institute, 2022.
3. O‘zbekiston Respublikasi Prezidentining 2024-yil 14-oktabrdagi PQ-358-son “Sun’iy intellekt texnologiyalarini 2030-yilgacha rivojlantirish strategiyasini tasdiqlash to‘g‘risida”gi Qarori.
4. O‘zbekiston Respublikasi Prezidentining 2025-yil 22-oktabrdagi PF-189-son “Sun’iy intellekt texnologiyalarini yanada rivojlantirishga oid qo‘shimcha chora-tadbirlar to‘g‘risida”gi Farmoni.
5. O‘zbekiston Respublikasi Prezidentining 2021-yil 17-fevraldagi PQ-4996-son “Sun’iy intellekt texnologiyalarini jadal joriy etish uchun shart-sharoitlar yaratish chora-tadbirlari to‘g‘risida”gi Qarori.
6. Haykin S. Neural Networks and Learning Machines. 3rd Edition. – New Jersey: Prentice Hall, 2009.
7. McCulloch W., Pitts W. A Logical Calculus of the Ideas Immanent in Nervous Activity // Bulletin of Mathematical Biophysics. – 1943. – Vol. 5. – P. 115–133.
8. Hebb D.O. The Organization of Behavior: A Neuropsychological Theory. – New York: Wiley, 1949.
9. Rosenblatt F. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain // Psychological Review. – 1958. – Vol. 65. – No. 6. – P. 386–408.
10. Minsky M., Papert S. Perceptrons. – Cambridge: MIT Press, 1969.
11. Rumelhart D., Hinton G., Williams R. Learning Representations by Back-Propagating Errors // Nature. – 1986. – Vol. 323. – P. 533–536.
12. Hochreiter S., Schmidhuber J. Long Short-Term Memory // Neural Computation. – 1997. – Vol. 9. – No. 8. – P. 1735–1780.
13. Goodfellow I., Bengio Y., Courville A. Deep Learning. – Cambridge: MIT Press, 2016.
14. Krizhevsky A., Sutskever I., Hinton G. ImageNet Classification with Deep Convolutional Neural Networks // Advances in Neural Information Processing Systems (NIPS). – 2012.
15. Terasvirta T. Specification, Estimation and Evaluation of Smooth Transition Autoregressive Models // Journal of the American Statistical Association. – 1994. – Vol. 89. – No. 425.
16. Huang W., Nakamori Y., Wang S.Y. Forecasting Stock Market Movement Direction with Support Vector Machine // Computers & Operations Research. – 2005. – Vol. 32. – No. 10.
17. Nakamori Y. Knowledge Science: Modeling the Knowledge Creation Process. – Boca Raton: CRC Press, 2011.
18. Oreshkin B.N., Carpov D., Chapados N., Bengio Y. N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting // International Conference on Learning Representations (ICLR). – 2020.
19. Bekmurodov U., Absalamova D., Absalamova G. Economic Forecasting Using Artificial Intelligence Models and Algorithms in the Case of Uzbekistan // Digital Transformation and Artificial Intelligence. – 2023.
20. Hamidova F.P., Ahmedov R.M. Sun’iy intellekt yordamida iqtisodiy ko‘rsatkichlarni prognozlash usullari // Ilmiy tadqiqotlar va innovatsiyalar jurnali. – 2023.
21. Kazarinova E.B. Application of LSTM and GRU Models in Macroeconomic Forecasting // Economic Systems Research. – 2025.
22. Tyshchenko V., Achkasova S., Naidenko O. Cryptocurrency Price Forecasting Using Recurrent Neural Networks // Kharkiv National Economic University Scientific Bulletin. – 2023.
23. Drozdovskaya P.K., Sikolenko M.A., Kachuk I.A. Neural Networks for Time Series Forecasting: Currency Exchange Rate Prediction // Belarus State University Scientific Proceedings. – 2024.
24. Dubovitskiy D.S. Forecasting Financial Time Series Using RNN, LSTM and GRU Models // Applied Informatics Journal. – 2020–2025.
25. Bekmurodov U., Majidov E.A. Big Data va Sun’iy Intellekt Asosida Valyuta Kurslarini Prognozlash // Zamonaviy Tadqiqotlar Jurnali. – 2025.
