基于前馈多层感知器神经网络的互联网时延建模与预测

Seyed Reza Seyed Tabib, A. Jalali
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引用次数: 8

摘要

对于许多应用,特别是基于Internet的远程操作系统,网络时延预测在提高动态性能方面起着关键作用。因此,必须着重研究网络时延的理想预测方法。本文在分析网络时延的基础上,提出了一种基于神经网络的网络不确定时延预测方法。利用四个Internet节点间的实测数据和MATLAB环境,采用单步超前预测算法对前馈多层感知器(FMLP)神经网络进行了训练。然后,利用验证数据对神经网络的性能进行评价。结果表明,该神经网络可以根据输入的合适程度来预测时延。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling and Prediction of Internet Time-Delay by Feed-Forward Multi-layer Perceptron Neural Network
Internet time-delay prediction plays a key role in improving dynamic performance for many applications, especially for Internet-based tele-operation systems. Therefore, ideal prediction approach for Internet time-delay must be investigated emphatically. In this paper, after analayzing Internet time-delay, a new approach based on neural network is developed to predict the uncertain time-delay in the Internet. By using measured data between four Internet nodes and MATLAB environment, the feed-forward multi-layer perceptron (FMLP) neural network has been trained with single-step-ahead Prediction algorithm. Then, using validation data, the performance of the neural network was evaluated. It is shown that this neural network can predict time-delay based on their proper inputs.
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