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Neural Network Training Using PSO Algorithm in ATM Traffic Control

  • Yuan-wei Jing
  • Tao Ren
  • Yu-cheng Zhou
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 344)

Abstract

In this paper, we address an end-to-end congestion control algorithm for available bit-rate traffic in high speed asynchronous transfer mode network. A neural network controller is proposed, because the precise characteristics of the switching system architecture are not known and some conditions such as time delay and network load change over time. The particle swarm optimization algorithm, which characterizes fast convergence and global minimum is introduced in neural network weights training. Simulation results show that the control system is adaptive, robust and effective, the quality of service is guaranteed.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Asynchronous Transfer Mode Neural Network Training Neural Network Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuan-wei Jing
    • 1
  • Tao Ren
    • 1
  • Yu-cheng Zhou
    • 2
  1. 1.Northeastern UniversityShenyang, LiaoningChina
  2. 2.Research Institute of Wood IndustryChinese Academy of ForestryBeijingChina

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