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Network Traffic Prediction Model Based on Mixed Kernels RVM Optimized by CS Algorithm

  

  1. (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)
  • Received:2014-12-25 Online:2015-05-18 Published:2015-05-18

Abstract: In order to improve the prediction accuracy of network traffic, a network traffic prediction model is proposed based on cuckoo searching algorithm optimizing the parameters of mixed kernel relevance vector machine (CS-RVM) to solve limitations of single kernel function for relevance vector machine. Firstly, the polynomial and Gaussian kernel functions are produced to mixed kernel function for the relevance vector machine, and then the cuckoo searching algorithm is introduced to optimize the parameters of hybrid kernel function, finally network traffic prediction model is established based on the relevance vector machine using the optimal parameters. The simulation results show that, CS-RVM model is of good effect and could improve the prediction accuracy of network traffic.

Key words: network traffic, prediction model, cuckoo search algorithm, relevance vector machine

CLC Number: