• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >Neural network architecture based on gradient boosting for IoT traffic prediction
【24h】

Neural network architecture based on gradient boosting for IoT traffic prediction

机译：基于梯度提升的神经网络架构用于物联网流量预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Network traffic forecasting is an operational and management function that is critical for any data network. It is even more important for IoT networks given the number of connected elements and the real-time nature of many connections. This work presents a novel deep learning architecture applicable to this supervised regression problem. It is based on an additive network model formed by 'learning blocks' that are stacked iteratively following, in part, the principles of gradient boosting models. The resulting architecture is trained end-to-end using stochastic gradient descent. This new architecture has connections with residual, stacked and boosted networks, being different from any of them. Like residual networks, it shows excellent convergence behavior during training and allows for deeper models. It has a regularization effect similar to stacked models and presents excellent prediction results as gradient boosting models do. The building elements of the architecture are neural network blocks or learning blocks, that can be constituted by a sequence of simple fully connected layers or by more elaborate dispositions of recurrent and convolutional layers. The resulting architecture is a generic additive network (gaNet) applicable to any supervised regression problem.To obtain experimental results on a hard prediction problem, the model is applied to the forecasting of network traffic using IoT traffic volume real data from a mobile operator. The paper presents a comprehensive comparison of results between the proposed new model and many alternative algorithms, showing important improvements in terms of prediction performance metrics and training/prediction processing times. (C) 2019 Elsevier B.V. All rights reserved.
机译：网络流量预测是一项运行和管理功能，对于任何数据网络都至关重要。考虑到连接元素的数量和许多连接的实时性，对于物联网网络而言，这一点甚至更为重要。这项工作提出了适用于此监督回归问题的新颖的深度学习架构。它基于由“学习块”形成的加性网络模型，这些“学习块”部分地遵循梯度提升模型的原理进行迭代堆叠。使用随机梯度下降对生成的体系结构进行端到端训练。这种新架构具有与残留，堆叠和增强型网络的连接，这与它们中的任何一个都不相同。像残差网络一样，它在训练过程中表现出出色的收敛行为，并允许使用更深层次的模型。它具有类似于堆叠模型的正则化效果，并且与梯度增强模型一样，具有出色的预测结果。该体系结构的构建元素是神经网络模块或学习模块，它们可以由一系列简单的完全连接的层或递归和卷积层的更精细的布置构成。由此产生的架构是适用于任何监督回归问题的通用加性网络（gaNet）。为了获得有关硬预测问题的实验结果，该模型使用来自移动运营商的IoT流量真实数据来预测网络流量。本文对提出的新模型和许多替代算法之间的结果进行了全面比较，显示了在预测性能指标和训练/预测处理时间方面的重要改进。 （C）2019 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号