Improving reliability of aggregation, numerical simulation and analysis of complex systems by empirical data

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: All-Russian Scientific and Practical Conference (with International Participation) on Automation Systems in Education, Science and Production; Novokuznetsk, RUSSIA; Novokuznetsk, RUSSIA

Год издания: 2018

Идентификатор DOI: 10.1088/1757-899X/354/1/012006

Аннотация: 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. © Published under licence by IOP Publishing Ltd.

Ссылки на полный текст

Издание

Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: Vol. 351, Is. 1

ISSN журнала: 17578981

Издатель: Institute of Physics Publishing

Авторы

  • Dobronets Boris S. (Siberian Fed Univ, Inst Space & Informat Technol, Kirenskogo 26, Krasnoyarsk 660074, Russia)
  • Popova Olga A. (Siberian Fed Univ, Inst Space & Informat Technol, Kirenskogo 26, Krasnoyarsk 660074, Russia)

Вхождение в базы данных

Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.

Вы можете отметить интересные фрагменты текста, которые будут доступны по уникальной ссылке в адресной строке браузера.