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Single Feedback Based Kernel Generalized Maximum Correntropy Adaptive Filtering Algorithm

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Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14447))

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Abstract

This paper presents a novel single feedback based kernel generalized maximum correntropy (SF-KGMC) algorithm by introducing a single delay into the framework of kernel adaptive filtering. In SF-KGMC, the history information implicitly existing in the single delayed output can enhance the convergence rate. Compared to the second-order statistics criterion, the generalized maximum correntropy (GMC) criterion shows better robustness against outliers. Therefore, SF-KGMC can efficiently reduce the influence of impulsive noise and avoids significant performance degradation. In addition, for SF-KGMC, the theoretical convergence analysis is also conducted. Simulation results on chaotic time-series prediction and real-world data applications validate that SF-KGMC achieves better filtering accuracy and a faster convergence rate.

This work is supported in part by the National Natural Science Foundation of China(Grant no. 62201478 and 61971100), in part by the Southwest University of Science and Technology Doctor Fund (Grant no. 20zx7119), in part by the Sichuan Science and Technology Program (Grant no. 2022YFG0148), and in part by the Heilongjiang Provincial Science and Technology Program (No. 2022ZX01A16).

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Notes

  1. 1.

    SF-KGMC-CC stands for SF-KGMC sparsified by the CC sparsification method.

  2. 2.

    http://www3.dsi.uminho.pt/pcortez/series/.

  3. 3.

    https://www.sidc.be/silso/datafiles.

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Author information

Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Jiaming Liu, Ji Zhao & Qiang Li

  2. Department of Social Sciences, Southwest University of Science and Technology, Mianyang, 621010, China

    Lingli Tang

  3. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hongbin Zhang

Authors
  1. Jiaming Liu
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  2. Ji Zhao
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  3. Qiang Li
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  4. Lingli Tang
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  5. Hongbin Zhang
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Corresponding authors

Correspondence to Ji Zhao or Qiang Li .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Liu, J., Zhao, J., Li, Q., Tang, L., Zhang, H. (2024). Single Feedback Based Kernel Generalized Maximum Correntropy Adaptive Filtering Algorithm. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_1

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  • DOI: https://doi.org/10.1007/978-981-99-8079-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8078-9

  • Online ISBN: 978-981-99-8079-6

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