Кафедра "Комп'ютерна математика і аналіз даних"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/7570

Офіційний сайт кафедри http://web.kpi.kharkov.ua/kmmm

Кафедра "Комп'ютерна математика і аналіз даних" заснована в 2002 році.

Кафедра входить до складу Навчально-наукового інституту комп'ютерних наук та інформаційних технологій Національного технічного університету "Харківський політехнічний інститут", забезпечує підготовку бакалаврів і магістрів за проектно-орієнтованою освітньою програмою за напрямом науки про дані "DataScience".

У складі науково-педагогічного колективу кафедри працюють: 3 доктора наук: 1 – технічних, 1 – фізико-математичних, 1 – педагогічних; 15 кандидатів наук: 10 – технічних, 4 – фізико-математичних, 1 – педагогічних; 3 співробітників мають звання професора, 9 – доцента.

Переглянути

Фільтри

2017 - 201912020 - 20241

Налаштування

ORCID: search.filters.orcid.https://orcid.org/0000-0002-9015-4016Ключові слова: search.filters.subject.fuzzy initial data

Результати пошуку

Зараз показуємо 1 - 2 з 2
  • Ескіз
    Документ
    Method for solving distributional problems of mathematical programming under conditions of fuzzy initial data
    (Technology center PC, 2024) Raskin, Lev; Sira, Oksana; Hatunov, Artur; Riabokon, Roman; Sinitsyn, Rodyslav
    The object of this study is a large class of mathematical programming problems under conditions of uncertainty of initial data. The formulated object generates a subclass of problems of rational distribution of a limited resource under conditions of initial data described in terms of fuzzy mathematics. The conventional, standardly used method for solving such problems is based on optimization on average. To obtain such a solution, it is sufficient to replace all fuzzy initial data with their modal values in the analytical description of the mathematical model of the corresponding problem. To solve the resulting deterministic problem, one can use known methods of mathematical programming. However, the results of such a solution can be used in practice if the carriers of fuzzy parameters are specified compactly, that is, the intervals of possible values of fuzzy parameters of the problem are small. Otherwise, the implementation of this solution may lead to unpredictably large losses. Other alternative approaches are based on the use of insufficiently informative estimates of the best or worst possible values of fuzzy parameters of the problem. These circumstances make the statement of the problem and the objective of the study relevant: devising a method for solving the problem of rational distribution of a limited resource under conditions of fuzzy initial data. To solve the stated problem of rational distribution of a limited resource, a productive idea of constructing the proposed general optimization method under conditions of uncertainty of the initial data has been constructively implemented. In this case, the initial problem is reduced to a clear problem of optimizing a complex criterion constructed on the basis of the objective function of the initial problem and a set of membership functions of its fuzzy parameters. An example of solving the problem has been considered, leading to a solution that is better than that obtained on the basis of the modal values of the fuzzy parameters of the problem.
  • Ескіз
    Документ
    Models and methods of regression analysis under conditions of fuzzy initial data
    (Technology center PC, 2017) Raskin, Lev; Sira, Oksana; Ivanchykhin, Yuriy
    The paper considers the problem of regression analysis with indeterminate explanatory and explained variables. A quality criterion for estimating the regression coefficients is formulated and justified, taking into account possible significant differences in the accuracy of assigning different variables. The study considers a method of calculating the regression coefficients in accordance with the concept of least squares. The proposed approach provides a reasonable compromise between the conflicting requirements: the maximum compactness of the fuzzy value function of the explained variable and the minimal deviation of the solution from the modal one. The problem is solved by minimizing the complex criterion, the terms of which determine the level of satisfaction of these requirements. An additional advantage of the approach is that the original problem, fuzzy by the nature of the initial data, is reduced to solving two usual problems of mathematical programming. The problem of fuzzy comparator identification is considered when the values of the explained variable are not defined but can be ranked by the descending of any chosen indicator. To solve this problem, the study proposes a method for estimating regression coefficients based on solving a fuzzy system of linear algebraic equations.