Adaptive information processing systems (122 Computer Science)
Type: For the student's choice
Department: radiophysics and computer technologies
Curriculum
Semester | Credits | Reporting |
9 | 8 | Setoff |
Lectures
Semester | Amount of hours | Lecturer | Group(s) |
9 | 32 | Associate Professor Liubun Z. M. | ФеІм-11 |
Laboratory works
Semester | Amount of hours | Group | Teacher(s) |
9 | 48 | ФеІм-11 | Associate Professor Rabyk V. G., Associate Professor Liubun Z. M. |
Опис навчальної дисципліни
The academic discipline “Adaptive Information Processing Systems” is an integral part of the disciplines of the cycle of elective disciplines for training specialists of the educational qualification level “master”. The discipline is designed to provide participants with the necessary knowledge to implement the basic algorithms of adaptive data analysis systems, including the use of neural networks, as well as to develop their skills in applying the acquired knowledge and algorithms in the implementation of adaptive systems. Therefore, the discipline presents the basics of adaptive filtering systems, adaptive control systems, data classification based on neural networks, data forecasting based on multilayer neural networks and radial basis functions, and the use of fuzzy sets in control systems.
After completing this course, the student will:
– know the main types of digital filters (recursive and nonrecursive), methods of their analysis and synthesis; the main types of adaptive filters, methods of their design, features of adaptive digital filtering; the main applications of adaptive digital filters; data prediction based on multilayer neural networks and radial basis function networks;
– be able to design nonrecursive and recursive digital filters, adaptive digital filters and perform their modeling; perform synthesis, analysis, and modeling of adaptive control systems, create emulators and analyze the operation of neural networks that solve problems of classification, clustering and data forecasting; have skills in operating programs for emulating neural network structures for information processing.
Recommended Literature
- Alex Becker. 2023. Kalman Filter Overview. [Електронний ресурс]. – Режим доступу: https://www.kalmanfilter.net/default.aspx
- Liubun Z. Hover Signal-Profile Detection / Liubun, V. Mandziy, H. Klein, O. Karpin, V. Rabyk // Proceedings of the XV International Scientific and Technical Conference “Computer Science and Information Technologies” – 2022. P. 7 – 10. (Scopus)
- Karpin O. Method of Neural Network Training with Integer Weights / O. Karpin, V. Mandziy, Z. Liubun, V. Rabyk // Proceedings of the XIth International Scientific and Practical Conference “Electronics and Information Technologies” (ELIT – 2019), September 16 – 18, 2019, Lviv, Ukraine. P. 168 – 172. doi: 10.1109/ELIT.2019.8893349.
- Rhudy M. B. Kalman filtering tutorial for undergraduate students. / M. B. Rhudy, R. A. Salguero, K. A. Holappa / Int. J. Comp. Sci. Eng. Surv. (1), 8 (2017).