首页 | 本学科首页   官方微博 | 高级检索  
     

一种基于自适应KLMS的卫星网络流量预测算法
引用本文:赵季红,王明欣,曲桦,谢志勇,刘熙. 一种基于自适应KLMS的卫星网络流量预测算法[J]. 北京邮电大学学报, 2018, 41(3): 51-55. DOI: 10.13190/j.jbupt.2017-144
作者姓名:赵季红  王明欣  曲桦  谢志勇  刘熙
作者单位:1. 西安邮电大学 通信与信息工程学院, 西安 710121;
2. 西安交通大学 电子与信息工程学院, 西安 710049
基金项目:国家高技术研究发展计划(863计划);国家自然科学基金;国家自然科学基金
摘    要:针对传统预测模型已不再适用于卫星网络的问题,提出了一种自适应步长和自适应核宽度的核最小均方算法(AKLMS).通过核函数将非线性数据从低维输入空间映射到高维特征空间进行操作,并且在迭代过程中根据瞬时误差自适应地调整步长和核宽度.仿真结果证明,与核最小均方算法(KLMS)和最小均方算法(LMS)相比,AKLMS算法在收敛速度和预测流量精度方面都有大幅提升,为卫星网络的流量规划和路由设计提供了强有力的决策支持.

关 键 词:卫星网络  核最小均方算法  自适应步长  自适应核宽度  网络流量预测  
收稿时间:2017-07-17

An Adaptive KLMS Traffic Prediction Algorithm for Satellite Network
ZHAO Ji-hong,WANG Ming-xin,QU Hua,XIE Zhi-yong,LIU Xi. An Adaptive KLMS Traffic Prediction Algorithm for Satellite Network[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(3): 51-55. DOI: 10.13190/j.jbupt.2017-144
Authors:ZHAO Ji-hong  WANG Ming-xin  QU Hua  XIE Zhi-yong  LIU Xi
Affiliation:1. School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an 710121, China;
2. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Abstract:Due to the resource limitation and topology change in satellite network, the article puts forward higher requirements for the accuracy and efficiency of the network traffic prediction algorithm, and the traditional prediction model is no longer suitable for the satellite network. The author presents a kernel least mean square algorithm (KLMS) with adaptive step length and adaptive kernel width, namely AKLMS, which maps the nonlinear data from low dimensional input space to high dimensional feature space through kernel function, and the algorithm will adaptively adjust the step length and kernel width based on the instantaneous error in the iterative process. Simulations show that the AKLMS algorithm has great improvement on the convergence speed and prediction accuracy of the flow compared with the KLMS and least mean square (LMS), which will provide strong decision support for traffic planning and routing design in satellite network.
Keywords:satellite network  kernel least mean square  adaptive step length  adaptive kernel width  network traffic prediction  
本文献已被 万方数据 等数据库收录!
点击此处可从《北京邮电大学学报》浏览原始摘要信息
点击此处可从《北京邮电大学学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号