Improving reliability of aggregation, numerical simulation and analysis of complex systems by empirical data
URI (for links/citations):http://iopscience.iop.org/article/10.1088/1757-899X/354/1/012006/meta
Boris, S Dobronets
Olga, A Popova
Институт космических и информационных технологий
Кафедра систем искусственного интеллекта
Journal Name:IOP Conference Series: Materials Science and Engineering
Journal Quartile in Scopus:без квартиля
Journal Quartile in Web of Science:без квартиля
Bibliographic Citation:Boris, S Dobronets. Improving reliability of aggregation, numerical simulation and analysis of complex systems by empirical data [Текст] / S Dobronets Boris, A Popova Olga // IOP Conference Series: Materials Science and Engineering: Materials Science and Engineering. — 2018. — Т. 354 (№ 012006).
The paper considers a new approach of regression modeling that uses aggregated data presented in the form of density functions. Approaches to Improving the reliability of aggregation of empirical data are considered: improving accuracy and estimating errors. We discuss the procedures of data aggregation as a preprocessing stage for subsequent to regression modeling. An important feature of study is demonstration of the way how represent the aggregated data. It is proposed to use piecewise polynomial models, including spline aggregate functions. We show that the proposed approach to data aggregation can be interpreted as the frequency distribution. To study its properties density function concept is used.Various types of mathematical models of data aggregation are discussed. For the construction of regression models, it is proposed to use data representation procedures based on piecewise polynomial models. New approaches to modeling functional dependencies based on spline aggregations are proposed.