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

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@Article{Chen2012274,

• 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 = "http://www.sciencedirect.com/science/article/pii/S1568494611003280",
• 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

Citations