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基于核自适应滤波器的时间序列在线预测研究综述

韩敏 马俊珠 任伟杰 钟凯

韩敏, 马俊珠, 任伟杰, 钟凯. 基于核自适应滤波器的时间序列在线预测研究综述. 自动化学报, 2021, 47(4): 730−746 doi: 10.16383/j.aas.c190051
引用本文: 韩敏, 马俊珠, 任伟杰, 钟凯. 基于核自适应滤波器的时间序列在线预测研究综述. 自动化学报, 2021, 47(4): 730−746 doi: 10.16383/j.aas.c190051
Han Min, Ma Jun-Zhu, Ren Wei-Jie, Zhong Kai. A survey of time series online prediction based on kernel adaptive filters. Acta Automatica Sinica, 2021, 47(4): 730−746 doi: 10.16383/j.aas.c190051
Citation: Han Min, Ma Jun-Zhu, Ren Wei-Jie, Zhong Kai. A survey of time series online prediction based on kernel adaptive filters. Acta Automatica Sinica, 2021, 47(4): 730−746 doi: 10.16383/j.aas.c190051

基于核自适应滤波器的时间序列在线预测研究综述

doi: 10.16383/j.aas.c190051
基金项目: 国家自然科学基金(61773087)资助
详细信息
    作者简介:

    韩敏:大连理工大学电子信息与电气工程学部教授. 主要研究方向为模式识别, 复杂系统建模及时间序列预测. 本文通信作者.E-mail: minhan@dlut.edu.cn

    马俊珠:大连理工大学电子信息与电气工程学部硕士研究生. 主要研究方向为时间序列在线建模, 预测.E-mail: majunzhu@mail.dlut.edu.cn

    任伟杰:大连理工大学电子信息与电气工程学部博士研究生. 主要研究方向为时间序列分析和特征选择.E-mail: renweijie@mail.dlut.edu.cn

    钟凯:大连理工大学电子信息与电气工程学部博士研究生. 主要研究方向为工业过程监控, 故障诊断.E-mail: zhongkai0402@mail.dlut.edu.cn

A Survey of Time Series Online Prediction Based on Kernel Adaptive Filters

Funds: Supported by National Natural Science Foundation of China (61773087)
More Information
    Author Bio:

    HAN Min Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers pattern recognition, modeling of complex system, and time series prediction. Corresponding author of this paper

    MA Jun-Zhu Master student at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers time series online modeling and prediction

    REN Wei-Jie Ph.D. candidate at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His research interest covers time series analysis and feature selection

    ZHONG Kai Ph.D. candidate at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His research interest covers industrial process monitoring and fault diagnosis

  • 摘要: 核自适应滤波器(Kernel adaptive filter, KAF)是时间序列在线预测的重点研究领域之一, 本文对核自适应滤波器的最新进展及未来研究方向进行了分析和总结. 基于核自适应滤波器的时间序列在线预测方法, 能较好地解决预测、跟踪问题. 本文首先概述了三类核自适应滤波器的基本模型, 包括核最小均方算法、核递归最小二乘算法和核仿射投影算法(Kernel affine projection algorithm, KAPA). 在此基础上, 从核自适应滤波器在线预测的内容和机理入手, 综述基于核自适应滤波器的时间序列在线预测方法. 最后, 本文将介绍这一领域潜在的研究方向和发展趋势, 并展望未来的挑战.
  • 图  1  从输入空间到特征空间的非线性映射f(·)

    Fig.  1  Nonlinear mapping f(·) from input space to feature space

    图  2  KAF方法分类框图

    Fig.  2  Classification diagram of the KAF method

    表  1  不同KAF方法的时间序列在线预测特性对比结果

    Table  1  Comparison of online prediction characteristics of time series of different KAF methods

    算法类型 预测效率 预测精度 收敛速度 特点
    KLMS[19] 较高 较低 较慢 泛化能力和正则化特性
    KRLS[20] 较低 较高 较快 白化处理, 收敛速度较快
    KAPA[21] 考虑多个样本, 降低梯度噪声
    下载: 导出CSV

    表  2  每次迭代过程涉及的计算复杂度比较

    Table  2  Comparison of computational complexity involved in each iteration

    核自适应滤
    波器类型
    在线稀
    疏类型
    计算复杂度
    KLMS[19] VQ 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {L^2} } )$
    在线VQ ${\rm O}( { {L} } )$
    SF 更新$ {\omega} \left( i \right) $ ${\rm O}( { {L} } )$
    更新$ {e}\left( i \right) $ ${\rm O}( { {L} } )$
    KRLS[20] VQ 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {L} } )$
    在线VQ ${\rm O}( { {L} } )$
    更新$ {P}\left( i \right) $ ${\rm O}( { {L^2} } )$
    ALD 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {L^2} } )$(${\rm O}( { {L^2} } )$
    假如字典改变)
    更新ALD ${\rm O}( { {L^2} } )$
    更新${P}\left( i \right)$ ${\rm O}( { {L^2} } )$
    SW 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {K^2} } )$(${\rm O}( { {K} } )$
    假如字典改变)
    更新${P}\left( i \right)$ ${\rm O}( { {K^2} } )$
    更新${D}\left( i \right)$ ${\rm O}( { {K^2} } )$
    FB 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {K^2} } )$(${\rm O}( { {K} } )$
    假如字典改变)
    更新${P}\left( i \right)$ ${\rm O}( { {K^2} } )$
    更新$ {{\hat K}_n}\left( i \right) $ ${\rm O}( { {K^2} } )$
    MF 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {L} } )$
    更新$ {e}\left( i \right) $ ${\rm O}( { {L} } )$
    更新${D}\left( i \right)$ ${\rm O}( { {L^2} } )$
    CC 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {K^2} } )$(${\rm O}( { {K} } )$
    假如字典改变)
    更新$ {e}\left( i \right) $ ${\rm O}( { {K^2} } )$
    更新${D}\left( i \right)$ ${\rm O}( { {K^2} } )$
    KAPA[21] VQ 更新$ {\alpha} \left( i \right) $ ${\rm O}( { {L} } )$
    在线VQ ${\rm O}( { {L} } )$
    更新${P}\left( i \right)$ ${\rm O}( { {L^2} } )$
    HC 更新$ {e}\left( i \right) $ ${\rm O}( { {L} } )$
    更新$ {\bf{\zeta }}\left( i \right) $ ${\rm O}( { {L} } )$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-21
  • 录用日期:  2019-09-24
  • 网络出版日期:  2020-01-02
  • 刊出日期:  2021-04-23

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