基于MLP和RBX的四模型神经网络高效路由算法

C. Anand
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引用次数: 0

摘要

利用无线路由协议进行路由信息的有效扩散和对动态网络条件的适应性这两个本质上矛盾的重要范式,是近年来研究的热点。解决这一问题的一种方法是利用节点过去在网络流量状况下的经验,通过智能算法来预测未来的网络流量状况。在这种方法中,我们提出了一种算法,用于预测路由过程中每个数据包的一跳延迟。预测的一跳延迟随后被参与节点进一步用于路由期间的信息扩散。实验分析表明,利用抽头延迟线径向基函数和抽头延迟线多层感知器可以将平均时延作为时间序列进行预测。用于预测的输入是包含交通负载的平均延迟时间序列和平均延迟时间序列本身。本文还提出了所建议工作的优点和缺点。
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Efficient Routing Algorithm using MLP and RBX in a Four Model Neural Networks
Two important paradigms which are contradicting by nature namely: the efficient routing information diffusion and adaptability to dynamic network conditions using wireless routing protocols have been researched in recent years. One way of solving this issue is by using the past experiences of a node in network traffic condition through intelligent algorithm to predict the network traffic condition in the future. In this methodology we propose an algorithm which is used to to predict one hop delay per packet during routing process using neural networking. The one hop delay that is predicted is then further used by the participating nodes for information diffusion during routing. Experimental analysis indicate that using tapped delay line radial basis function and tapped delay line multilayer perceptron, it is possible to predict mean delays as a time series. The inputs used for prediction are mean delay time series with traffic loads and mean delay time series itself. The pros and cons of the proposed work are also present in this paper.
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