dc.creatorDyllon, Shwan
dc.creatorHong, Timothy
dc.creatorOumar, Ousmane Abdoulaye
dc.creatorXiao, Perry
dc.date2018-09-30
dc.date.accessioned2023-08-07T20:09:03Z
dc.date.available2023-08-07T20:09:03Z
dc.identifierhttps://revistas.utp.ac.pa/index.php/memoutp/article/view/1919
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7940520
dc.descriptionTime series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work on London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on Levenberg-Marquardt backpropagation algorithm. This technique can analyse and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherUniversidad Tecnológica de Panamáes-ES
dc.relationhttps://revistas.utp.ac.pa/index.php/memoutp/article/view/1919/2861
dc.rightsDerechos de autor 2018 Memorias de Congresos UTPes-ES
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0es-ES
dc.sourceMemorias de Congresos UTP; 2018: The 21st International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines - CLAWAR 2018; 251-261es-ES
dc.titleEducational bandwidth traffic prediction using non-linear autoregressive neural networkses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArtículo revisado por pareses-ES


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