Earticle

초록

영어

Considering the shortcomings of the conventional BP neural network, such as slow learning speed, weak anti-interference ability and easy to fall into local minimum, the detection accuracy of P2P traffic detection model is low and the speed is slow, the particle swarm optimization algorithm is used to optimize it here. As the conventional algorithm's optimization ability is the initial parameters, the algorithm is easy to be early, and the convergence speed is slow. Therefore, grouping, organizing, fission and mutation operation on the conventional algorithm have been carried on in order to improve the defect of conventional algorithm. Finally, the P2P traffic detection model is built by using MATLAB software, and traffic detection experiments are carried out on Bittorrent, EMule, PPlive and PPStream 4 P2P network applications. The test data show that the average recognition rate of the recognition model is 96.14%, which is 13.3% higher than that of the conventional PSO-BP model, and9.4% higher than that of the QPSO-BP recognition model for the four P2P network applications.

목차

Abstract
 1. Introduction
 2. Improve BP Neural Network
  2.1. BP Neural Network
  2.2. Improved Particle Swarm Optimization (PSO)
 3. Research on Experiment
 4. Conclusion
 References

키워드

저자

• Song Rong [ Jiangxi Vocational and Technical College of Communication, Nanchang, Jiangxi, China ]
• Li Xiating [ Jiangxi Vocational and Technical College of Communication, Nanchang, Jiangxi, 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