Earticle

초록

영어

With the rapid development of Internet technology, the network now has a large size and high complexity, and consequently the network management is becoming increasing difficult and complexity, so traffic forecast play a more and more role in network management. With a large amount of real traffic data collected from the actual network, an adaptive network traffic prediction algorithm based on BP neural network was proposed in this paper, it use an adaptive learning rate method to adjust the learning rate according to total error changing trend of decreased or increased and the difference of changing; and then it corrects the weights in each layers according to forward and reverse calculation. Simulation results show that, compared with the traditional BP neural network, our algorithm has better performance in the prediction results, and has smaller error.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Adaptive Network Traffic Prediction Method based on BP Neural Network
  3.1. Network Traffic Measurement Method
  3.2. Back-Propagation (BP) Neural Network Model
  3.3. Training Algorithm of Weights in Layers
  3.4. Improved BP Algorithm
 4. Simulation Results
  4.1. Performance Computation
  4.2. Computation of Prediction Value with Actual Value
 Acknowledgements
 References

키워드

저자

• Ming Zhang [ Department of Electronic Engineering, Huaihai Institute of Technology; Lian Yungang, China ]
• Yanhong Lu [ Department of Mechanical and Electronic Engineering, Lianyungang Technical College Lian Yungang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605