Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Information Technology Journal
Year: 2011  |  Volume: 10  |  Issue: 12  |  Page No.: 2322 - 2328

Research on Fuzzy Self-adaptive Variable-weight Combination Prediction Model for IP Network Traffic

Minsheng Tan, Hui Xu, Lin Zeng and Shiying Xia    

Abstract: In combination prediction of IP network traffic, the single model’s mathematical characteristic, prediction accuracy and weight coefficient have significant impact on combination prediction results. As the grey model can depict linearity characteristics of network traffic and the BP neural network model can depict the non-stationary and non-linear characteristics, a Fuzzy Self-Adaptive Variable-Weight Combination Prediction Model (FSVCPM) was composed of them. To improve the prediction accuracy of single model as far as possible, a improved residual grey prediction model was established via indexation processing of residual sequence. By training experiments, neuron number of input layer and hidden layer was identified and corresponding BP neural network was given. By introducing fuzzy decision mechanism and self-adaptive mechanism to calculate fuzzy weight and basic weight, FSVCPM was built and a determination method of variable-weight coefficient was addressed which can make single models to fit effectively. Experimental results validated the correctness and accuracy of the FSVCPM and proved the prediction precision was higher than that of the single model and the Constant-Weight Combination Prediction Model (CCPM).

Cited References   |    Fulltext    |   Related Articles   |   Back
 
 
   
 
 
 
  Related Articles

 
 
 
 
 
 
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility