基于混沌理论与改进回声状态网络的网络流量多步预测

田中大; 李树江; 王艳红; 王向东

doi:10.11959/j.issn.1000-436x.2016053

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基于混沌理论与改进回声状态网络的网络流量多步预测

学术论文 | 更新时间：2024-06-05

- 基于混沌理论与改进回声状态网络的网络流量多步预测
- Network traffic multi-step prediction based on chaos theory and improved echo state network
- 通信学报 2016年37卷第3期页码：55-70
- 作者机构：
  
  沈阳工业大学信息科学与工程学院，辽宁沈阳110870
- 作者简介：
  
  [ "田中大（1978-），男，辽宁沈阳人，博士，沈阳工业大学讲师，主要研究方向为时间序列建模与预测、网络控制系统时延补偿、非线性系统预测控制等。" ]
  [ "李树江（1966-），男，辽宁沈阳人，博士，沈阳工业大学教授，主要研究方向为复杂工业过程建模与控制、智能控制技术的应用研究与开发等。" ]
  [ "王艳红（1967-），女，辽宁沈阳人，博士，沈阳工业大学教授，主要研究方向为生产调度与优化、先进制造信息系统、分布式信息处理与智能控制。" ]
  [ "王向东（1959-），男，辽宁沈阳人，博士，沈阳工业大学教授，主要研究方向为生产调度与优化、复杂工业过程建模与控制、智能控制技术的应用研究与开发。" ]
- 基金信息：
  
  国家自然科学重点基金资助项目(61034005);辽宁省博士启动基金资助项目(20141070)
- DOI：10.11959/j.issn.1000-436x.2016053
  中图分类号： TP393
- 网络出版日期：2016-03，
  
  纸质出版日期：2016-03-25
- 稿件说明：
移动端阅览
田中大, 李树江, 王艳红, 等. 基于混沌理论与改进回声状态网络的网络流量多步预测[J]. 通信学报, 2016,37(3):55-70.

Zhong-da TIAN, Shu-jiang LI, Yan-hong WANG, et al. Network traffic multi-step prediction based on chaos theory and improved echo state network[J]. Journal on communications, 2016, 37(3): 55-70.
田中大, 李树江, 王艳红, 等. 基于混沌理论与改进回声状态网络的网络流量多步预测[J]. 通信学报, 2016,37(3):55-70. DOI： 10.11959/j.issn.1000-436x.2016053.

Zhong-da TIAN, Shu-jiang LI, Yan-hong WANG, et al. Network traffic multi-step prediction based on chaos theory and improved echo state network[J]. Journal on communications, 2016, 37(3): 55-70. DOI： 10.11959/j.issn.1000-436x.2016053.

摘要

网络流量预测是网络管理及网络拥塞控制的重要问题，针对该问题提出一种基于混沌理论与改进回声状态网络的网络流量预测方法。首先利用0-1混沌测试法与最大Lyapunov指数法对不同时间尺度下的网络流量样本数据进行分析，确定网络流量在不同时间尺度下都具有混沌特性。将相空间重构技术引入网络流量预测，通过C-C 方法确定延迟时间，G-P算法确定嵌入维数。对网络流量时间序列进行相空间重构之后，利用一种改进的回声状态网络进行网络流量的多步预测。提出一种改进的和声搜索优化算法对回声状态网络的相关参数进行优化以提高预测精度。利用网络流量的公共数据集以及实际数据进行了仿真，结果表明，提出的预测方法具有更高的预测精度以及更小的预测误差。

Abstract

Network traffic prediction was an important problem of network management and network congestion control.In order to solve this problem

a network traffic prediction method based on chaos theory and improved echo state net-work was proposed.Firstly

network traffic sample with differe time scale were analyzed by 0-1 test algorithm for chaos and maximum Lyapunov exponent

the calculation results show that the network traff has chaotic characteristics in different time scale.The phase space reconstruction technique was introduced for the prediction of network traffic

the delay time was determined through the C-C method

the embedding dimension was determined through the G-P algo-rithm.Network traffic time series was processed with phase space reconstruction

the multi-step prediction of network traffic was achieved by an improved echo state network.In order to improve the prediction precision

the key dynamic reservoir and prediction parameters of echo state network were optimized by an improved harmony search algorithm.Through the simulation on public and actual network traffic data

the results verify the proposed prediction method has higher prediction accuracy and smaller prediction error.

关键词

Keywords

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