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Educational bandwidth traffic prediction using non-linear autoregressive neural networks

dc.contributores-ES
dc.creatorDyllon, Shwan
dc.creatorHong, Timothy
dc.creatorOumar, Ousmane Abdoulaye
dc.creatorXiao, Perry
dc.date2018-09-30
dc.date.accessioned2018-12-04T14:32:11Z
dc.date.available2018-12-04T14:32:11Z
dc.identifierhttp://revistas.utp.ac.pa/index.php/memoutp/article/view/1919
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5763
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.relationhttp://revistas.utp.ac.pa/index.php/memoutp/article/view/1919/2861
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
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.subjectEducational bandwidth;traffic predictiones-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


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