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经验模式分解与时间序列分析在网络流量预测中的应用
  • ISSN号：1001-0920
  • 期刊名称：《控制与决策》
  • 时间：0
  • 分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]沈阳工业大学信息科学与工程学院,沈阳110870, [2]东北大学信息科学与工程学院,沈阳110004
  • 相关基金：国家自然科学基金重点项目（61034005）.
作者： 田中大[1], 李树江[1], 王艳红[1], 高宪文[2]
关键词： 网络流量, 经验模式分解, 时间序列, 白相似, 预测, network traffic, empirical mode decomposition, time series, self-similar, prediction
中文摘要：

提出一种经验模式分解和时间序列分析的网络流量预测方法．首先，对网络流量时间序列进行经验模式分解，产生高低频分量和余量；然后，对各分量进行时间序列分析，确保高频分量采用改进和声搜索算法优化的最小二乘支持向量机模型、低频分量和余量采用差分白回归滑动平均模型进行建模和预测；最后，将预测结果通过RBF神经网络进行非线性叠加，得到最终的预测值．仿真实验表明，所提出方法具有更好的预测效果和更高的预测精度．

英文摘要：

A network traffic prediction method based on empirical mode decomposition and time series self-similar analysis is proposed. Firstly, network traffic time-series high and low frequency components are generated by empirical mode decomposition. Then the component time series is analyzed to determine that the least squares support vector machine model optimized by using the improved harmony search algorithm is used for high frequency components modeling, and the auto regressive integrated moving average model is used for low frequency components modeling and remaining component modeling. Finally, the final prediction result is obtained by RBF neural network nonlinear superposition. Simulation results show that the proposed method has better prediction results and higher prediction accuracy.

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期刊信息
  • 《控制与决策》
  • 北大核心期刊（2011版）
  • 主管单位:中华人民共和国教育部
  • 主办单位:东北大学
  • 主编：张嗣瀛 王福利
  • 地址：沈阳市东北大学125信箱
  • 邮编：110004
  • 邮箱：kzyjc@mail.neu.edu.cn
  • 电话：024-83687766
  • 国际标准刊号：ISSN：1001-0920
  • 国内统一刊号：ISSN：21-1124/TP
  • 邮发代号:8-51
  • 获奖情况:
  • 1997年被评为辽宁省优秀编辑部,1999年期刊影响因子在信息与系统类期刊中排名第二位
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国数学评论（网络版）,德国数学文摘,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:32961