Applying RBF Network to Predict Location in Mobile Network

Ming Lei, Pilian He, Zhichao Li
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Abstract

In mobile network, quality of service (Qos) is difficultly guaranteed for the particularity of mobile network. If the system knows, prior to the mobile subscriber movement, the exact trajectory it will follow, the Qos can be guaranteed. Thus, location prediction is the key issue to provide quality of service to mobile subscriber. In the present paper, RBF Network of Neural Network techniques were used to predict the mobile user's next location based on his current location as well as time. The software matlab 6.5 was used to confirm the parameters of RBF network, and to same training data, makes the detailed contrast with resilient propagation BP and BP in learning time and steps of learning. Experiment results show that predicted locations with RBF are more effective and accurate than resilient BP.
应用RBF网络预测移动网络中的位置
在移动网络中,由于移动网络的特殊性,服务质量(Qos)很难得到保证。如果系统在移动用户移动之前知道它将遵循的确切轨迹,则可以保证Qos。因此,位置预测是向移动用户提供高质量服务的关键问题。本文利用神经网络技术中的RBF网络,根据移动用户当前的位置和时间预测下一个位置。利用matlab 6.5软件对RBF网络的参数进行了确定,并对相同的训练数据,与弹性传播BP和BP在学习时间和学习步骤上进行了详细对比。实验结果表明,基于RBF的位置预测比弹性BP更有效、准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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