一种基于FNN的高速网络拥塞控制策略

Policy of Fuzzy Neural Network Based Congestion Control in High-Speed Network

以 ATM( asynchronous transfer mode)为研究对象 ,提出一种基于模糊神经网络 ( fuzzy neural network,简称 FNN)的流量预测和拥塞控制策略 .拥塞控制是高速网络 (如 ATM)研究中的关键问题之一 .传统的基于 BP神经网络的流量预测方法因其收敛速度较慢且具有较大的误差 ,影响了拥塞控制效果 ,而模糊神经网络由于具有处理不确定性问题和很强的学习能力 ,能很好地解决这一问题 .最后通过仿真 ,比较和分析了基于 BP神经网络和基于 FNN方法的性能 。

A kind of traffic prediction and congestion control policy based on FNN (fuzzy neural network) is proposed for ATM (asynchronous transfer mode). Congestion control is one of the key problems in high-speed networks, such as ATM. Conventional traffic prediction method for congestion control using BPN (back propagation neural network) has suffered from long convergence time and dissatisfying precision, and it is not effective. The fuzzy neural network scheme presented in this paper can solve these limitations satisfactorily for its good capability of processing inaccurate information and learning. Finally, the performance of the scheme based on BPN is compared with the scheme based on FNN using simulations. The results show that the FNN scheme is effective.