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

Authors:
Yuehui Chen;Bin Yang;Qingfang Meng
Affiliations:
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information science and Engineering, University of Jinan, 106 Jiwei Road, 250022 Jinan, PR China;Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information science and Engineering, University of Jinan, 106 Jiwei Road, 250022 Jinan, PR China;Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information science and Engineering, University of Jinan, 106 Jiwei Road, 250022 Jinan, PR China
Venue:
Applied Soft Computing
Year:
2012

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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 optimized by the Particle Swarm Optimization 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 optimized by PSO for the same problem.