Predictive estimation of mathematical models of information spreading process under uncertainty

Autor:	Iuliia Shevchuk, Oleksandr Nakonechnyi, Petro Zinko
Rok vydání:	2017
Předmět:	Set (abstract data type) Computational Theory and Mathematics Artificial Intelligence Applied Mathematics Ordinary differential equation Applied mathematics Type (model theory) Theoretical Computer Science Mathematics
Zdroj:	System research and information technologies. :54-65
ISSN:	2308-8893 1681-6048
DOI:	10.20535/srit.2308-8893.2017.4.05
Popis:	The mathematical model of spreading the information of an arbitrary number of types was considered. The model takes the form of a system of non-linear ordinary differential equations with stationary parameters. Special cases of presenting observation errors are considered. The algorithms for building averaged optimal rms predictive estimation and guaranteed predictive estimation are offered. The algorithm for building averaged optimal rms predictive estimation for a case of spreading information of one type and the algorithm for finding guaranteed predictive estimation for a particular case of representing a set of possible observation errors are obtained. The results of a numerical experiment for the problem of building guaranteed predictive estimates for the system with two sources of information are considered.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1cf9db20917b40a3344352806d932e0c https://doi.org/10.20535/srit.2308-8893.2017.4.05 Zobrazit plný text záznamu