Kernel Least Mean Square With Maximum Correntropy Criterion

Yawen Li; Wenling Li; Zhe Xue; Ang Li

doi:10.1109/CCIS57298.2022.10016417

Kernel Least Mean Square With Maximum Correntropy Criterion

Yawen Li
, Wenling Li
, Zhe Xue
, Ang Li

School of Automation Science and Electrical Engineering

Beijing University of Posts and Telecommunications

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We introduce a novel kernel least mean square (KLMS) algorithm for nonlinear input-output models, where the output is generated with respect to multiple inputs in a coupled fashion. The KLMS algorithm is proposed under maximum correntropy criterion for robustness. The mean square convergence has been carried out and the energy conservation relation is also established, which reflect the effects of the coupling parameter. A data-independent upper bound on the stepsize is derived to guarantee the convergence of the KLMS algorithm. Simulation results are provided to demonstrate the excellent performance.

Original language	English
Title of host publication	Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022
Editors	Fuji Ren, Witold Pedrycz, Zhiquan Luo, Dan Yang, Tianrui Li, Mengqi Zhou, Weining Wang, Aijing Li, Dandan Dandan, Liu Yaru Zou, Yanna Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	554-557
Number of pages	4
ISBN (Electronic)	9781665477352
DOIs	https://doi.org/10.1109/CCIS57298.2022.10016417
State	Published - 2022
Event	8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022 - Chengdu, China Duration: 26 Nov 2022 → 28 Nov 2022

Publication series

Name	Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

Conference

Conference	8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022
Country/Territory	China
City	Chengdu
Period	26/11/22 → 28/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Kernel adaptive filter
Least mean square
Maximum correntropy criterion

Access to Document

10.1109/CCIS57298.2022.10016417

Cite this

Li, Y., Li, W., Xue, Z., & Li, A. (2022). Kernel Least Mean Square With Maximum Correntropy Criterion. In F. Ren, W. Pedrycz, Z. Luo, D. Yang, T. Li, M. Zhou, W. Wang, A. Li, D. Dandan, L. Y. Zou, & Y. Liu (Eds.), Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022 (pp. 554-557). (Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCIS57298.2022.10016417

Li, Yawen ; Li, Wenling ; Xue, Zhe et al. / Kernel Least Mean Square With Maximum Correntropy Criterion. Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022. editor / Fuji Ren ; Witold Pedrycz ; Zhiquan Luo ; Dan Yang ; Tianrui Li ; Mengqi Zhou ; Weining Wang ; Aijing Li ; Dandan Dandan ; Liu Yaru Zou ; Yanna Liu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 554-557 (Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022).

@inproceedings{aaf4c8748f2b4375a26313cf8f0f22c6,

title = "Kernel Least Mean Square With Maximum Correntropy Criterion",

abstract = "We introduce a novel kernel least mean square (KLMS) algorithm for nonlinear input-output models, where the output is generated with respect to multiple inputs in a coupled fashion. The KLMS algorithm is proposed under maximum correntropy criterion for robustness. The mean square convergence has been carried out and the energy conservation relation is also established, which reflect the effects of the coupling parameter. A data-independent upper bound on the stepsize is derived to guarantee the convergence of the KLMS algorithm. Simulation results are provided to demonstrate the excellent performance.",

keywords = "Kernel adaptive filter, Least mean square, Maximum correntropy criterion",

author = "Yawen Li and Wenling Li and Zhe Xue and Ang Li",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022 ; Conference date: 26-11-2022 Through 28-11-2022",

year = "2022",

doi = "10.1109/CCIS57298.2022.10016417",

language = "英语",

series = "Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "554--557",

editor = "Fuji Ren and Witold Pedrycz and Zhiquan Luo and Dan Yang and Tianrui Li and Mengqi Zhou and Weining Wang and Aijing Li and Dandan Dandan and Zou, \{Liu Yaru\} and Yanna Liu",

booktitle = "Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022",

address = "美国",

}

Li, Y, Li, W, Xue, Z & Li, A 2022, Kernel Least Mean Square With Maximum Correntropy Criterion. in F Ren, W Pedrycz, Z Luo, D Yang, T Li, M Zhou, W Wang, A Li, D Dandan, LY Zou & Y Liu (eds), Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022. Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 554-557, 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022, Chengdu, China, 26/11/22. https://doi.org/10.1109/CCIS57298.2022.10016417

Kernel Least Mean Square With Maximum Correntropy Criterion. / Li, Yawen; Li, Wenling; Xue, Zhe et al.
Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022. ed. / Fuji Ren; Witold Pedrycz; Zhiquan Luo; Dan Yang; Tianrui Li; Mengqi Zhou; Weining Wang; Aijing Li; Dandan Dandan; Liu Yaru Zou; Yanna Liu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 554-557 (Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Kernel Least Mean Square With Maximum Correntropy Criterion

AU - Li, Yawen

AU - Li, Wenling

AU - Xue, Zhe

AU - Li, Ang

PY - 2022

Y1 - 2022

N2 - We introduce a novel kernel least mean square (KLMS) algorithm for nonlinear input-output models, where the output is generated with respect to multiple inputs in a coupled fashion. The KLMS algorithm is proposed under maximum correntropy criterion for robustness. The mean square convergence has been carried out and the energy conservation relation is also established, which reflect the effects of the coupling parameter. A data-independent upper bound on the stepsize is derived to guarantee the convergence of the KLMS algorithm. Simulation results are provided to demonstrate the excellent performance.

AB - We introduce a novel kernel least mean square (KLMS) algorithm for nonlinear input-output models, where the output is generated with respect to multiple inputs in a coupled fashion. The KLMS algorithm is proposed under maximum correntropy criterion for robustness. The mean square convergence has been carried out and the energy conservation relation is also established, which reflect the effects of the coupling parameter. A data-independent upper bound on the stepsize is derived to guarantee the convergence of the KLMS algorithm. Simulation results are provided to demonstrate the excellent performance.

KW - Kernel adaptive filter

KW - Least mean square

KW - Maximum correntropy criterion

UR - https://www.scopus.com/pages/publications/85147665698

U2 - 10.1109/CCIS57298.2022.10016417

DO - 10.1109/CCIS57298.2022.10016417

M3 - 会议稿件

AN - SCOPUS:85147665698

T3 - Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

SP - 554

EP - 557

BT - Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

A2 - Ren, Fuji

A2 - Pedrycz, Witold

A2 - Luo, Zhiquan

A2 - Yang, Dan

A2 - Li, Tianrui

A2 - Zhou, Mengqi

A2 - Wang, Weining

A2 - Li, Aijing

A2 - Dandan, Dandan

A2 - Zou, Liu Yaru

A2 - Liu, Yanna

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

Y2 - 26 November 2022 through 28 November 2022

ER -

Li Y, Li W, Xue Z, Li A. Kernel Least Mean Square With Maximum Correntropy Criterion. In Ren F, Pedrycz W, Luo Z, Yang D, Li T, Zhou M, Wang W, Li A, Dandan D, Zou LY, Liu Y, editors, Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 554-557. (Proceedings of 2022 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022). doi: 10.1109/CCIS57298.2022.10016417

Kernel Least Mean Square With Maximum Correntropy Criterion

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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