Small-time scale network traffic prediction based on flexible neural tree

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Yuehui Chen and Bin Yang and Qingfang Meng",
  title =        "Small-time scale network traffic prediction based on
                 flexible neural tree",
  journal =      "Applied Soft Computing",
  volume =       "12",
  number =       "1",
  pages =        "274--279",
  year =         "2012",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2011.08.045",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Flexible
                 neural tree model, Particle Swarm Optimization, Network
                 traffic, Small-time scale",
  abstract =     "In this paper, the flexible neural tree (FNT) model is
                 employed to predict the small-time scale traffic
                 measurements data. Based on the pre-defined
                 instruction/operator sets, the FNT model can be created
                 and evolved. This framework allows input variables
                 selection, over-layer connections and different
                 activation functions for the various nodes involved.
                 The FNT structure is developed using the Genetic
                 Programming (GP) and the parameters are optimised by
                 the Particle Swarm Optimisation algorithm (PSO). The
                 experimental results indicate that the proposed method
                 is efficient for forecasting small-time scale traffic
                 measurements and can reproduce the statistical features
                 of real traffic measurements. We also compare the
                 performance of the FNT model with the feed-forward
                 neural network optimised by PSO for the same problem.",

Genetic Programming entries for Yuehui Chen Bin Yang Qingfang Meng