A Network Traffic Hybrid Prediction Model Optimized by Improved Harmony Search Algorithm

Title:

A Network Traffic Hybrid Prediction Model Optimized by Improved Harmony Search Algorithm

Creator:

Tian , Z., Li , S., Wang, Y., and Wang, X.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f3568ccb-799e-49e2-a122-015e96e10c8d
uuid:f3568ccb-799e-49e2-a122-015e96e10c8d
doi:10.14311/NNW.2015.25.034

Subject:

network traffic, grey model, Elman neural network, prediction, and improved harmony search

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The telecommunication and Ethernet trafic prediction problem is studied. Network traffic prediction is an important problem of telecommunication and Ethernet congestion control and network management. In order to improve network traffic prediction accuracy, a network traffic hybrid prediction model was proposed by using the advantages of grey model and Elman neural network, grey model and Elman neural network predictive values were independently obtained, the different weight coefficients of two prediction models were given. In terms of weight coefficients optimization, an improved harmony search algorithm with better convergence speed and accuracy was proposed, the optimal weight coefficients of network traffic hybrid prediction model were determined through this algorithm, two prediction models results were multiplied by the weight coefficients to obtain the final prediction value. The network traffic sample data from an actual telecommunication network was collected as simulation object. The simulation results verified that the proposed network traffic hybrid prediction model based on improved harmony search algorithm has higher prediction accuracy.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:6

Harvested from:

CDK

Metadata only:

false