基于Additive-multiplicative模糊神经网的ATM网络拥塞控制  

Congestion control in ATM networks based on additive-multiplicative fuzzy neural network

作　　者：翟东海[1] 李力[1] 靳蕃[1] 

机构地区：[1]西南交通大学计算机与通信工程学院,四川成都610031

出　　处：《控制与决策》2004年第6期651-654,共4页Control and Decision

摘　　要：考虑了模糊神经网络的学习功能,提出利用Additive multiplicative模糊神经网络(AMFNN)对ATM网络进行拥塞控制的方案.在拥塞控制过程中,利用AMFNN模糊神经网络预测下一个将要到达流的特征,结合当前缓冲区的队列信息预测网络是否发生拥塞.一旦预测出将有拥塞发生,控制器则向源端反馈拥塞控制信息,信源根据拥塞信息适当降低传输速率,从而避免了拥塞的发生.仿真结果表明,该方法可改善网络对拥塞的实时处理能力,提高网络资源的利用率.Based on additive-multiplicative fuzzy neural network (AMFNN), a congestion control scheme for asynchronous transfer mode (ATM) network is presented. This scheme uses AMFNN to accurately predict the traffic arrival patterns. The predicted traffic with the current queue information of the buffer can be used as a measure of congestion. When the congestion level is reached, a control signal is generated to throttle the input arrival rate. The AMFNN model and its learning algorithm are discussed. The simulation results show that this method can improve the congestion processing capability in real time, and raise the utilization of the network resource.

关 键 词：ATM网络 模糊神经网络 拥塞控制 信元丢失率 

分 类 号：TP11[自动化与计算机技术—控制理论与控制工程]