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

作　　者：何小燕[1,2] 吴介一[1,2] 顾冠群[1,2] 

机构地区：[1]教育部计算机网络和信息集成重点实验室,江苏南京210096 [2]东南大学计算机科学与工程系,江苏南京210096

出　　处：《软件学报》2001年第1期41-48,共8页Journal of Software

基　　金：国家重点基础研究发展规划资助项目! (G19980 30 40 5 )

摘　　要：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.

关 键 词：拥塞控制 流量预测 模糊神经网络 高速网络 ATM FNN 计算机网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]