Published December 23, 2025 | Version v1

REAL VAQT DATA STREAMLARNI QAYTA ISHLASHDA AI ALGORITMLARINING SAMARADORLIGI

  • 1. Muhandislik falsafa doktori (PhD)
  • 2. Farg'ona Davlat Texnika Universitetining birinchi kurs magistri

Description

Ushbu maqola real vaqt data streamlarni qayta ishlashda sun'iy intellekt (AI) algoritmlarining samaradorligini tahlil qiladi. Tadqiqotning maqsadi – turli AI yondashuvlari, mashinani o'rganish, chuqur neyron tarmoqlar va reinforcement learning algoritmlarining real vaqt ma'lumot oqimlarini qayta ishlashdagi natijaviyligini aniqlash. Maqolada data streamlarning o'ziga xos xususiyatlari, sensor ma'lumotlarini yig'ish, shovqinlarni filtratsiya qilish va qaror qabul qilish jarayonlari ko'rib chiqiladi. Real vaqt AI algoritmlari ma'lumotlar oqimini tahlil qilish, tizim javob tezligini oshirish va resurslardan samarali foydalanish imkonini beradi. Tadqiqot natijalari sanoat robotlari, avtonom transport va aqlli ishlab chiqarish tizimlari uchun amaliy tavsiyalar beradi.

Files

38_984-237-242-Ergashev.pdf

Files (381.6 kB)

Name Size
md5:954d30a8cff047936602fb2d32192d53
381.6 kB Preview Download

Additional details

References

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
  • Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • Chen, T., Li, M., & Smola, A. J. (2015). Big data and AI for autonomous systems: Methods and applications. Journal of Robotics and Autonomous Systems, 70, 1–12. https://doi.org/10.1016/j.robot.2015.03.001
  • Camacho, E. F., & Bordons, C. (2013). Model predictive control (2nd ed.). Springer.
  • Paden, B., Čáp, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016). A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions on Intelligent Vehicles, 1(1), 33–55.
  • Thrun, S., Burgard, W., & Fox, D. (2021). Probabilistic robotics (2nd ed.). MIT Press.
  • Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end training of deep visuomotor policies. Journal of Machine Learning Research, 17(39), 1–40.
  • Gao, F., & Wang, J. (2018). Sensor fusion techniques for autonomous robots: A review. Robotics and Autonomous Systems, 107, 11–23. https://doi.org/10.1016/j.robot.2018.02.003
  • Bifet, A., & Gavaldà, R. (2007). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining, 443–448.