Abstract
Ushbu maqola tarmoq trafigini shifrlaydigan Ethernet kartasiga
integratsilanuvchi apparat majmuasining arxitekturasini ishlab chiqish TCP/IP protokoli
stekiga asoslangan paketli kommutatsiyalangan kompyuter tarmog‘ining (Ethernet)
trafikni boshqarish to‘g‘irisida. Ethernet kartasi texnologiyasidan foydalanishning
bugungi kundagi ahamiyati: tarmoq trafigini shifrlaydigan Ethernet kartasi
arxitekturasini shakllantirish va kartaga integratsiyalovchi apparat majmuasini
yechimlarga bag‘ishlangan.
References
1. Vishnevskiy, VM Markazlashtirilmagan boshqaruv bilan simsiz tarmoqlarni modellashtirish [Matn] / VM Vishnevskiy, AI Lyaxov, BN Tereshchenko // Avtomatlashtirish va telemexanika. - 1999. - No 6. - B. 88–99.
2. Verbitskiy, S. N. Xizmat ko‘rsatish stavkasini boshqarishning optimal siyosatini raqamli o‘rganish [Matn] / S. N. Verbitskiy, V. V. Rikov // Avtomatlashtirish va telemexanika. - 1998. - No 11. - B. 59–70.
3. Ivnitskiy, V. A. O‘tish ehtimoli uning holatiga bog‘liq bo‘lgan yulduz shaklidagi yopiq navbat tarmog‘i holatining statsionar ehtimolliklari to‘g‘risida [Matn] / V. A. Ivnitskiy // Avtomatlashtirish va kompyuter texnologiyalari. - 1994. - No 6. - B. 29–37.
4. Bakanov, AC Markazlashtirilgan boshqaruvga ega simsiz tarmoqlarning ishlash ko‘rsatkichlarini baholash usuli [Matn] / AC Bakanov, V. M. Vishnevskiy, A. I. Lyaxov // Avtomatlashtirish va telemexanika. - 2000. - No 4. - B. 97-105.
5. Gibbens, RJ Ko‘p parentli tarmoqlarda dinamik marshrutlash [Matn] / R. Gibbens, FP Kelly, SRE Turner // IEEE / Networking bo‘yicha ACM tranzaksiyalari. - 1993. - jild. 1, iss. 2. - B. 261–270.
6. Korilis, YA Stackelberg marshrutlash strategiyalari yordamida tarmoq optimalligiga erishish [Matn] / YA Korilis, AA Lazar, A. Orda // Networking bo‘yicha IEEE / ACM tranzaktsiyalari. – 1997.
7. H. Zaynidinov, O. Mallayev, Parallel algorithm for calculating the learning processes of an artificial neural network. AIP Conference Proceedings 2647, 050006 (2022). doi: https://doi.org/10.1063/5.0104178.
8. Yusupov I, Nurmurodov J, Ibragimov S, Gofurjonov M, Qobilov S. “Calculation of Spectral Coefficients of Signals on the Basis of Haar by the Method of Machine Learning”, 14th International Conference, IHCI 2022, Tashkent, Uzbekistan, October 20–22, 2022, pp 547–558. https://link.springer.com/conference/ihci.
9. Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010, Springer.
10. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
11. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
13. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer.
14. Shukla, P. (2019). The Gradient Descent Algorithm and Its Variants. arXiv preprint arXiv:1908.10448. doi: 10.1093/ptep/ptaa104.
15. https://www.baeldung.com/cs/gradient-stochastic-and-mini-batch.
This work is licensed under a Creative Commons Attribution 4.0 International License.