Ensemble of Flexible Neural Tree and Ordinary Differential Equations for Small-time Scale Network Traffic Prediction

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@Article{Yang:2013:academy,
  author =       "Bin Yang and Mingyan Jiang and Yuehui Chen and 
                 Qingfang Meng and Ajith Abraham",
  title =        "Ensemble of Flexible Neural Tree and Ordinary
                 Differential Equations for Small-time Scale Network
                 Traffic Prediction",
  journal =      "Journal of Computers",
  publisher =    "ACADEMY PUBLISHER",
  year =         "2013",
  volume =       "8",
  number =       "12",
  pages =        "3039--3046",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, hybrid
                 evolutionary method, small-time scale network traffic,
                 the additive tree models, ordinary differential
                 equations, ensemble learning",
  ISSN =         "1796-203X",
  bibsource =    "OAI-PMH server at doaj.org",
  identifier =   "1796-203X; 10.4304/jcp.8.12.3039-3046",
  language =     "English",
  oai =          "oai:doaj.org/article:3a06be9c4660483dbaca0959705a4a18",
  URL =          "http://ojs.academypublisher.com/index.php/jcp/article/view/10007",
  DOI =          "doi:10.4304/jcp.8.12.3039-3046",
  abstract =     "Accurate models play important roles in capturing the
                 salient characteristics of the network traffic,
                 analysing and simulating for the network dynamic, and
                 improving the predictive ability for system dynamics.
                 In this study, the ensemble of the flexible neural 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 optimised based on
                 the Genetic Programming (GP) and the Particle Swarm
                 Optimisation algorithm (PSO). Finally, the expected
                 level of performance is verified by using 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
                 decent accuracy.",
}

Genetic Programming entries for Bin Yang Mingyan Jiang Yuehui Chen Qingfang Meng Ajith Abraham

Citations