SUN’IY INTELLEKT VA MATEMATIK MODELLASHTIRISHGA ASOSLANGAN RAQAMLI SIGNALLARNI QAYTA ISHLASHDA ILG‘OR SHOVQINNI KAMAYTIRISH USULLARI

Mualliflar

  • Abdubakir Abdullaev Muallif
  • Xo‘jamiyor Teshaboyev Muallif
  • Ro‘ziali Sobirov Muallif
  • Shoxijahon Ahmedov Muallif

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sun’iy intellekt, shovqinni kamaytirish, raqamli signalni qayta ishlash, FIR, IIR, neyron tarmoqlar, adaptiv filtrlar

Abstrak

Ushbu maqolada radiokanallardagi shovqin tufayli signal sifatining pasayishi va bit xato darajasi ning oshishi muammosi tahlil qilindi. Shovqin manbalari sifatida termik shovqin, atmosferik shovqin, interferensiya va ko‘p yo‘lli tarqalish effekti ko‘rib chiqilgan. Muammoni yechish uchun matematik modellashtirish, filtrlar va sun’iy intellekt modellaridan foydalanish asosida shovqinli nochiziqli signalini tozalash va original signalni tiklash jarayoni tahlil qilingan. Shovqinli raqamli signalni qayta ishlash va uning sifat ko‘rsatkichlarini yaxshilash maqsadida 1D CNN modeliga asoslangan dasturiy yechim ishlab chiqilgan. Modelning tuzilishi, o‘qitish bosqichlari hamda signalni filtrlash jarayonidagi vizual va miqdoriy natijalari tahlil qilingan. O‘tkazilgan tajribalar asosida taklif etilgan yondashuv bit xato darajasini sezilarli kamaytirishi va radio, mobil aloqa tizimlarida signal sifati oshishiga xizmat qilishimumkinligini keltirilgan.

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Bibliografik havolalar

1. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. https://doi.org/10.1007/978-0-387-45528-0

2. Goldsmith, A. (2005). Wireless communications. Cambridge University Press. https://doi.org/10.1017/CBO9780511841224

3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org

4. Haykin, S. (2002). Adaptive filter theory (4th ed.). Prentice Hall.

5. O’Shea, T. J., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563–575. https://doi.org/10.1109/TCCN.2017.2758370

6. Proakis, J. G., & Salehi, M. (2008). Digital communications (5th ed.). McGraw-Hill.

7. Simon, D. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Wiley-Interscience.

8. Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. https://doi.org/10.1109/TIP.2017.2662206

Nashr qilingan

2025-12-22

Nashr

Bo'lim

Technical Sciences

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