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JOURNALS // Avtomatika i Telemekhanika // Archive

Avtomat. i Telemekh., 2021 Issue 7, Pages 133–146 (Mi at15383)

This article is cited in 6 papers

Intellectual Control Systems, Data Analysis

An adaptive method for assessing traffic characteristics in high-speed multiservice communication networks based on a fuzzy control procedure

S. A. Ageeva, A. A. Privalovb, V. V. Karetnikovc, A. A. Butsanetsc

a Radioavionica JSC, St. Petersburg, 190103 Russia
b Emperor Alexander I St. Petersburg State Transport University, St. Petersburg, 190031 Russia
c Admiral Makarov State University of Maritime and Inland Shipping, St. Petersburg, 198035 Russia

Abstract: We propose an adaptive method and a vector algorithm for assessing the main characteristics of traffic in high-speed multiservice communication networks. The adaptive algorithm for assessing the characteristics of network traffic operates online. The method is based on the conditionally nonlinear Pareto-optimal filtering principle, in which the estimation of the unknown traffic parameters is performed in two stages. At the first stage, we estimate the value of the latest forecast function of unknown traffic parameters, and at the second stage, the forecast is corrected. An analysis of the research results for the method and algorithm proposed for assessing the main characteristics of traffic in high-speed multiservice communication networks has shown their high efficiency. The average relative error of the resulting estimates does not exceed 10% of the current values of the traffic characteristics being estimated.

Keywords: high-speed multiservice communication network, pseudogradient algorithm, conditionally nonlinear Pareto-optimal filtering, Takagi-Sugeno fuzzy inference, new generation communication network.

Presented by the member of Editorial Board: O. N. Granichin

Received: 20.11.2019
Revised: 25.02.2021
Accepted: 16.03.2021

DOI: 10.31857/S0005231021070072


 English version:
Automation and Remote Control, 2021, 82:7, 1222–1232

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© Steklov Math. Inst. of RAS, 2024