引用本文:韩敏,夏慧娟.基于核递归最大总广义相关熵的时间序列预测[J].控制理论与应用,2024,41(10):1944~1950.[点击复制]
HAN Min,XIA Hui-juan.Kernel recursive maximum total generalized correntropy for time series prediction[J].Control Theory and Technology,2024,41(10):1944~1950.[点击复制]
基于核递归最大总广义相关熵的时间序列预测
Kernel recursive maximum total generalized correntropy for time series prediction
摘要点击 2296  全文点击 42  投稿时间：2022-11-17  修订日期：2024-07-23
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  10.7641/CTA.2023.21017
  2024,41(10):1944-1950
中文关键词  核自适应滤波器  总广义相关熵  矢量量化  时间序列  预测
英文关键词  kernel adaptive filter  total generalized correntropy  vector quantization  time series  prediction
基金项目  国家自然科学基金项目(62173063)资助.
作者单位E-mail
韩敏* 大连理工大学 minhan@dlut.edu.cn 
夏慧娟 大连理工大学  
中文摘要
      针对核自适应滤波器(KAF)在非高斯和脉冲噪声环境下预测性能下降问题, 本文提出了一种新颖的鲁棒算法, 称为核递归最大总广义相关熵(KRMTGC)算法. 首先, 简要介绍系统模型和最大总相关熵(MTC)准则; 其次, 在核空间采用灵活的总广义相关熵准则取代MTC准则, 详细推导出KRMTGC算法, 该算法对异常值或非高斯噪声具有更强的鲁棒性; 此外, 为进一步控制KRMTGC算法中核矩阵无限扩张模式, 采用矢量量化思想降低计算复杂度,提出量化KRMTGC算法; 然后, 研究分析KRMTGC算法的局部收敛特性; 最后, 通过在基准Rossler系统和真实厄尔尼诺–南方涛动时间序列预测中的仿真结果表明: 相比其他KAF算法, 所提算法具有更优的预测速度和预测精度.
英文摘要
      In order to address the problem that the predictive performance of kernel adaptive filter (KAF) is degraded in non-Gaussian and impulsive noise environments, a novel robust algorithm called kernel recursive maximum total generalized correntropy (KRMTGC) algorithm is proposed. Firstly, the system model and maximum total correntropy (MTC) are briefly introduced. Secondly, the flexible total generalized correntropy criterion is utilized to replace the MTC criterion in the kernel space, and the KRMTGC algorithm is derived in detail, which exhibits enhanced robustness against outliers and non-Gaussian noise. Moreover, to further control the infinite expansion mode of the kernel matrix of the KRMTGC algorithm, the quantized KRMTGC algorithm is proposed by using vector quantization to reduce the computational complexity. Then, the local convergence performance of the KRMTGC algorithm is analyzed. Finally, simulation results of the benchmark Rossler system and the real-world El Nino-Southern Oscillation time series prediction demonstrate that the proposed algorithm outperforms other KAF algorithms in terms of prediction speed and accuracy.