期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
基于神经网络的ATM网络多媒体流拥塞控制
1
作者 杜树新 袁石勇 《电路与系统学报》 CSCD 北大核心 2007年第4期53-59,共7页
提出了一种在用户-网络接口(UNI)处利用神经网络方法实现ATM网络多媒体流拥塞控制的新方法。在该方法中,控制器输出为信源编码率及其对应的用户百分比,即根据信源编码率及对应的用户百分比调整进入复用缓冲器多媒体流速率,从而克服了以... 提出了一种在用户-网络接口(UNI)处利用神经网络方法实现ATM网络多媒体流拥塞控制的新方法。在该方法中,控制器输出为信源编码率及其对应的用户百分比,即根据信源编码率及对应的用户百分比调整进入复用缓冲器多媒体流速率,从而克服了以往拥塞控制方法中仅仅调整编码率带来的对所有信源进行整体调整的缺陷,使控制系统在信元丢失率最小情况下保证了多媒体流的质量,从而有效地利用了网络资源。本文还给出了两种实现方式,方式1中,神经网络输出层变量包括编码率及对应用户百分比,由连续编码率量化成离散值;方式2中,神经网络输出层变量只有连续的编码率,然后通过一定的换算公式计算出离散的编码率和对应的用户数。这两种实现方式中,方式1较为直观,但比方式2复杂。对话音流、视频流的仿真表明方法的有效性。 展开更多
关键词 拥塞控制 神经网络 ATM网络 多媒体流
下载PDF
Congestion control for ATM multiplexers using neural networks:multiple sources/single buffer scenario
2
作者 杜树新 袁石勇 《Journal of Zhejiang University Science》 EI CSCD 2004年第9期1124-1129,共6页
A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate ... A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality. 展开更多
关键词 Congestion control ATM networks Neural networks Source coding rate
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部