Kernel Adaptive Filtering Prediction Algorithm of Chaotic Time Series

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Kernel Adaptive Filtering Prediction Algorithm of Chaotic Time Series

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 01 Page number: 24-30 Column: Public date: 2022-12-06

Title:: Kernel Adaptive Filtering Prediction Algorithm of Chaotic Time Series

Author(s):: LIU Qiang; WANG Shiyuan; HUANG Xuewei; WANG Daili; College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China

Keywords:: prediction of chaotic time series; kernel adaptive filtering; generalized logarithmic kernel loss; Nyström mapping; recursive update

CLC:: TN911. 7

DOI:: 10.13705/j.issn.1671-6833.2023.01.001

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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Last Update: 2022-12-06