Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2020

Идентификатор DOI: 10.1051/e3sconf/202022302012

Аннотация: The article deals with the problem of modeling stochastic processes under uncertainty. The peculiarity of the processes under consideration is that the researcher does not have information about the mathematical structure of the object; the object is represented as a black box. The article proposes to use a nonparametric modeling algorithm based on a nonparametric estimate of the regression function on observations. To improve the accuracy of modeling, it is proposed to use an algorithm for generating training samples. The algorithm differs from the previous modification by the definition of essential variables. The results of computational experiments have shown the effectiveness of the proposed algorithms.

Журнал: REGIONAL PROBLEMS OF EARTH REMOTE SENSING (RPERS 2020)

Выпуск журнала: Vol. 223

ISSN журнала: 22671242

Место издания: CEDEX A

Издатель: E D P SCIENCES

• Chzhan Ekaterina (Siberian Fed Univ, Sch Informat & Space & Technol, Krasnoyarsk 660074, Russia)

• Web of Science Core Collection
• Ядро РИНЦ (eLIBRARY.RU)