Ensemble of flexible neural tree and ordinary differential equations for small-time scale network traffic prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86092835" target="_blank" >RIV/61989100:27240/13:86092835 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.4304/jcp.8.12.3039-3046" target="_blank" >http://dx.doi.org/10.4304/jcp.8.12.3039-3046</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4304/jcp.8.12.3039-3046" target="_blank" >10.4304/jcp.8.12.3039-3046</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ensemble of flexible neural tree and ordinary differential equations for small-time scale network traffic prediction

Popis výsledku v původním jazyce

Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexibleneural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified byusing the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with

Název v anglickém jazyce

Ensemble of flexible neural tree and ordinary differential equations for small-time scale network traffic prediction

Popis výsledku anglicky

Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexibleneural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified byusing the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Computers

ISSN

1796-203X

e-ISSN

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Svazek periodika

8

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

3039-3046

Kód UT WoS článku

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EID výsledku v databázi Scopus

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