Infopaket

Theory of evaluation of systems in conditions of uncertainty

Вибіркова дисципліна для ОП

First

2022/2023

7 Semester

3

Know the basic approaches to solving problems of assessing the characteristics of systems in the presence of uncertainties. Be able to calculate estimates of system parameters in the presence of uncertainties depending on the amount of a priori information. Demonstrate skills of interaction with other people, ability to work in teams. Be able to organize their own activities and get results within a limited time. Demonstrate the ability to self-study and continue professional development.

Distance form

Know: probability theory, probability processes and mathematical statistics, data analysis. Be able to: apply knowledge of probability theory and mathematical statistics, data analysis. Have basic skills: solve problems in probability theory and mathematical statistics, data analysis.

The discipline "Theory of evaluation of systems in conditions of uncertainty" is an integral part of the cycle of professional training of specialists of educational and qualification level "bachelor"; it includes the following sections: Weighted least squares method and its analysis. Markov's assessment and its possibilities. Estimation with a minimum standard error. The method of maximum likelihood. Minimax approach in evaluation theory. Estimates of Bayes and the maximum of the apothecary probability. Estimation of non-stationary system parameters. Particular attention is paid to gaining experience in the practical use of estimates depending on the amount of a priori information. Discipline is the discipline of free choice of the student.

1. Albert A. Regression, pseudoinversion and recurrent estimation / A. Albert. - М .: Наука, 1977. 2. Brammer K. Kalman-Bussey filter / K. Brammer, G. Ziffling. - M .: Nauka, 1982. 3. Grop D. Methods of system identification / D. Grop. - M .: Mir, 1979. 4. Kirichenko NF Minimax filters in problems of estimation of states, identification of parameters and recognition of images / NF Kirichenko, AS Slabospitsky // Cybernetics and Computer Science, 1985, vol. 65. 5. Ljung L. System Identification: Theory for the User / L. Ljung. - Englewood Cliffs, NJ: Prentice-Hall, 1987. 6. Karabutov NN Structures in identification problems. Construction and analysis - URSS. –2016 - 312 p. 7. SV Sokolov, SM Kovalev, PM Kucherenko, Yu.A. Smirnov. Methods for identifying fuzzy and stochastic systems. Fizmatlit. - 2018. - 432 p.

Lecture, individual work

Test work, exam.

Ukrainian