ANALYSIS OF FUZZY NEURAL CONTROL SYSTEMS FOR TELECOMMUNICATION NETWORKS

Alevtina Aleksandrovna Muradova; Svetlana Aleksandrovna Sadchikova

Авторы

Alevtina Aleksandrovna Muradova TUIT named after Muhammad al-Khwarizmi, PhD, associate professor of the Department of Telecommunication Engineering Автор
Svetlana Aleksandrovna Sadchikova TUIT named after Muhammad al-Khwarizmi, PhD, associate professor of the Department of Telecommunication Engineering Автор

Ключевые слова:

artificial neural networks (ANN), model complex relationships, two-dimensional vector, fuzzy logic algorithms, real-time control systems, data structure.

Аннотация

The article presents an analysis of fuzzy neural control systems for telecommunication networks. Neural networks and fuzzy logic can solve problems that are beyond the capabilities of traditional control systems in network telecommunications. This article examines the operation of such systems and the advantages they provide in areas such as high-speed processing of images, files, packets, and other types of multimedia information.

Библиографические ссылки

1. Bishop, C.M. (2006). Pattern Recognition and Machine Learning. New York: Springer. ISBN 978-0-387-31073-2.

2. Ferrie, C., & Kaiser, S. (2019). Neural Networks for Babies. Sourcebooks. ISBN 978-1-4926-7120-6.

3. Bozinovski, S. (2014). Modeling mechanisms of cognition-emotion interaction in artificial neural networks. Procedia Computer Science. p. 255-263.

4. Jospin, L.V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M. (2022). Hands-On Bayesian Neural Networks-A Tutorial for Deep Learning Users. IEEE Computational Intelligence Magazine. Vol. 17, №2. pp. 29-48. doi: 10.1109/mci.2022.3155327.

5. Ferreira, C. (2006). Designing Neural Networks Using Gene Expression Programming. Applied Soft Computing Technologies: The Challenge of Complexity. Springer-Verlag. pp. 517-536.

6. Alaloul, W.S., & Qureshi, A.H. (2019). Data Processing Using Artificial Neural Networks. Dynamic Data Assimilation - Beating the Uncertainties. doi: 10.5772/intechopen.91935.

7. Ganesan, N. (2010). Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data. International Journal of Computer Applications. 1 (26): 81 - 97. doi:10.5120/476-783.

8. Nabian, M.A., & Meidani, H. (2017). Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks. Computer-Aided Civil and Infrastructure Engineering. 33 (6): 443 - 458. doi:10.1111/mice.12359. S2CID 36661983.

9. Weng, J. (2012). Natural and Artificial Intelligence: Introduction to Computational Brain-Mind. Wayback Machine, BMI Press, ISBN 978-0-9858757-2-5.

10. Tahmasebi, H. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & Geosciences. 42: 18-27. doi: 10.1016/j.cageo.2012.02.004.

11. Bansod, N.A, Kulkarni, M., & Patil, S. H. (2005). Soft Computing- A Fuzzy Logic Approach. In Bharati Vidyapeeth College of Engineering (ed.). Soft Computing. Allied Publishers. p. 73. ISBN 978-81-7764-632-0.

12. Gerla, G. (2016). Comments on some theories of fuzzy computation. International Journal of General Systems. 45 (4): 372–392. doi: 10.1080/03081079.2015.1076403.

13. Francesco, V. (2017). Fuzzy Multiple Attribute Conditions in fsQCA: Problems and Solutions. Sociological Methods & Research. 49 (2): 312-355. doi: 10.1177/0049124117729693.

14. Di, S., & Bruno, N. (2013). On the Need of a Standard Language for Designing Fuzzy Systems. Studies in Fuzziness and Soft Computing. Vol. 296. pp. 3-15. doi:10.1007/978-3-642-35488-5.