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Avtomatika i Telemekhanika, 2017, Issue 5, Pages 56–70 (Mi at14799)  

This article is cited in 6 scientific papers (total in 6 papers)

Stochastic Systems, Queuing Systems

Forecasting anticipated incomes in the Markov networks with positive and negative customers

M. A. Matalytski

Yanka Kupala State University of Grodno, Grodno, Belarus
Full-text PDF (733 kB) Citations (6)
References:
Abstract: A method for determination of the time dependence of the anticipated income in the systems of Markov queuing networks with incomes and positive and negative customers was proposed. Subject to this proviso, the incomes from the transitions between the network states are deterministic functions depending on its state and time, and the system incomes in a time unit when they do not change their states depend only on these states. An illustrative example of calculations was given which shows that the anticipated incomes of the network systems can be both increasing and decreasing time functions assuming positive and negative values.
Keywords: Markov network, positive and negative customers, anticipated income, method of successive approximations.
Presented by the member of Editorial Board: A. I. Lyakhov

Received: 22.08.2015
English version:
Automation and Remote Control, 2017, Volume 78, Issue 5, Pages 815–825
DOI: https://doi.org/10.1134/S0005117917050046
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: M. A. Matalytski, “Forecasting anticipated incomes in the Markov networks with positive and negative customers”, Avtomat. i Telemekh., 2017, no. 5, 56–70; Autom. Remote Control, 78:5 (2017), 815–825
Citation in format AMSBIB
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  • https://www.mathnet.ru/eng/at14799
  • https://www.mathnet.ru/eng/at/y2017/i5/p56
  • This publication is cited in the following 6 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Avtomatika i Telemekhanika
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    Abstract page:201
    Full-text PDF :49
    References:33
    First page:18
     
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