计算机技术与发展Issue(2)：107-110,4.DOI:10.3969/j.issn.1673-629X.2014.02.026
基于队列长度和速率的拥塞控制神经网络方法
Method of Congestion Control Neural Network Based on Queue Length and Rate
摘要
Abstract
Study the problems of network congestion control. PID controller is an effective method of realizing the network congestion control,which completes the active queue management for network. According to the queue length and rate,using the traditional PID function implemented by neural network,a new network congestion control algorithm (RSPID) based on the queue length and the rate is proposed. The new algorithm adjusts the control parameters by using weighted momentum gradient learning algorithm in neural network, to overcome the adaptability and stability problems in traditional PID control caused by constant controller parameters. The simulation re-sults show that performance of the RSPID algorithm is superior to PID algorithm.关键词
拥塞控制/动量梯度学习/神经网络/比例微分积分器Key words
congestion control/momentum gradient learning/neuron network/PID分类
信息技术与安全科学引用本文复制引用
李琴,周井泉,黄亮亮..基于队列长度和速率的拥塞控制神经网络方法[J].计算机技术与发展,2014,(2):107-110,4.基金项目
江苏省自然科学基金项目(CXLX12_0471) （CXLX12_0471）
