[1]刘强,王世元,黄雪微,等.混沌时间序列的核自适应滤波预测算法[J].郑州大学学报(工学版),2023,44(01):24-30.[doi:10.13705/j.issn.1671-6833.2023.01.001]
　LIU Qiang,WANG Shiyuan,HUANG Xuewei,et al.Kernel Adaptive Filtering Prediction Algorithm of Chaotic Time Series[J].Journal of Zhengzhou University (Engineering Science),2023,44(01):24-30.[doi:10.13705/j.issn.1671-6833.2023.01.001]
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混沌时间序列的核自适应滤波预测算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年01期
页码:
24-30
栏目:
出版日期:
2022-12-06

文章信息/Info

Title:
Kernel Adaptive Filtering Prediction Algorithm of Chaotic Time Series
作者:
刘强; 王世元; 黄雪微; 王代丽
西南大学电子信息工程学院,重庆 400715

Author(s):
LIU Qiang;  WANG Shiyuan;  HUANG Xuewei;  WANG Daili
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
关键词:
Keywords:
prediction of chaotic time series;  kernel adaptive filtering;  generalized logarithmic kernel loss;  Nyström mapping;  recursive update
分类号:
TN911. 7
DOI:
10.13705/j.issn.1671-6833.2023.01.001
文献标志码:
A
摘要:
在实际环境中,混沌时间序列常包含大量的噪声和异常值。由于这些干扰因素,基于二阶相似性度量的核自适应滤波器在混沌时间序列预测中的预测性能显著下降。基于上述问题,提出了一种鲁棒混沌时间序列的核自适应滤波预测算法。所提算法基于广义对数核损失函数的非线性相似性度量,有效地提高了核自适应滤波器在脉冲噪声环境中的鲁棒性,与此同时,该算法采用自适应K-Means 采样的稀疏Nyström 非线性映射方法,预先固定了算法的网络尺寸,从而降低核自适应滤波算法的计算复杂度。在所提算法中,使用递归更新方式,使算法具备较快的收敛速度。最后对滤波算法进行Mackey-Glass 混沌时间序列的预测仿真。仿真结果表明:作为一种新的鲁棒KMeans采样的Nyström 递归最小广义对数核损失预测方法,与稀疏化核自适应滤波算法相比,该算法在脉冲噪声中具备更好的鲁棒性;与其他典型鲁棒核自适应滤波预测算法相比,该算法具备更快的收敛速度和更高的滤波精度。
Abstract:
In practical environment, chaotic time series often contain a lot of noise and outliers. Because of these interference factors, the prediction performance of the kernel adaptive filter based on the second-order similarity measure could decrease significantly in chaotic time series prediction. Based on the above problems, a robust kernel adaptive filter prediction algorithm for chaotic time series was proposed. The proposed algorithm based on the nonlinear similarity measure of the generalized logarithmic kernel loss function, could improve the robustness of the kernel adaptive filter in impulsive noise environment effectively. At the same time, the algorithm adopted adaptive K-Means sampling sparse Nyström nonlinear mapping method, which could fix the network size of the algorithm in advance, and thus reduce the computational complexity of the kernel adaptive filter algorithm. Through the recursive updating method, the proposed algorithm had faster convergence speed. Finally, the Mackey-Glass chaotic time series prediction simulation was carried out for the adaptive filter algorithms. The simulation results showed that, as a new robust K-Means sampling Nyström recursive minimum generalized logarithmic kernel loss prediction method, compared with sparse kernel adaptive filter algorithm, this algorithm had better robustness in impulsive noise. Compared with other typical robust kernel adaptive filter prediction algorithms, this algorithm had faster convergence rate and higher filtering accuracy.

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更新日期/Last Update: 2022-12-06