Diffusion approximated kernel least mean P-power algorithm

Wei Gao; Jie Chen; Lingling Zhang

doi:10.1109/ICSPCC46631.2019.8960839

Diffusion approximated kernel least mean P-power algorithm

Wei Gao, Jie Chen, Lingling Zhang

School of Marine Science and Technology

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

3 Scopus citations

Abstract

Considering nonlinear and non-Gaussian environments that are frequently encountered in real-world scenarios, we thus propose the approximated kernel least mean p-power (AKLM-P) algorithm to address these tough factors for nonlinear distributed systems. In order to efficiently devise this algorithm, we use the property of shift-invariant kernel function that can be approximated by random Fourier features, and consider the impulsive noise characterized by the α-stable distribution. The stability in the mean of proposed distributed algorithm is then studied. Finally, the superiorities of proposed approach compared to diffusion kernelleast-mean-square (KLMS) algorithm are confirmed via the experimental results.

Original language	English
Title of host publication	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728117072
DOIs	https://doi.org/10.1109/ICSPCC46631.2019.8960839
State	Published - Sep 2019
Event	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 - Dalian, Liaoning, China Duration: 20 Sep 2019 → 22 Sep 2019

Publication series

Name	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Conference

Conference	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Country/Territory	China
City	Dalian, Liaoning
Period	20/09/19 → 22/09/19

Keywords

Distributed diffusion
Impulsive noise
Kernel least mean p-power
Random Fourier features
Stability analysis

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10.1109/ICSPCC46631.2019.8960839

Cite this

Gao, W., Chen, J., & Zhang, L. (2019). Diffusion approximated kernel least mean P-power algorithm. In 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 Article 8960839 (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSPCC46631.2019.8960839

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title = "Diffusion approximated kernel least mean P-power algorithm",

abstract = "Considering nonlinear and non-Gaussian environments that are frequently encountered in real-world scenarios, we thus propose the approximated kernel least mean p-power (AKLM-P) algorithm to address these tough factors for nonlinear distributed systems. In order to efficiently devise this algorithm, we use the property of shift-invariant kernel function that can be approximated by random Fourier features, and consider the impulsive noise characterized by the α-stable distribution. The stability in the mean of proposed distributed algorithm is then studied. Finally, the superiorities of proposed approach compared to diffusion kernelleast-mean-square (KLMS) algorithm are confirmed via the experimental results.",

keywords = "Distributed diffusion, Impulsive noise, Kernel least mean p-power, Random Fourier features, Stability analysis",

author = "Wei Gao and Jie Chen and Lingling Zhang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 ; Conference date: 20-09-2019 Through 22-09-2019",

year = "2019",

month = sep,

doi = "10.1109/ICSPCC46631.2019.8960839",

language = "英语",

series = "2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019",

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Gao, W, Chen, J & Zhang, L 2019, Diffusion approximated kernel least mean P-power algorithm. in 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019., 8960839, 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, Dalian, Liaoning, China, 20/09/19. https://doi.org/10.1109/ICSPCC46631.2019.8960839

Diffusion approximated kernel least mean P-power algorithm. / Gao, Wei; Chen, Jie; Zhang, Lingling.
2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8960839 (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019).

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

TY - GEN

T1 - Diffusion approximated kernel least mean P-power algorithm

AU - Gao, Wei

AU - Chen, Jie

AU - Zhang, Lingling

PY - 2019/9

Y1 - 2019/9

N2 - Considering nonlinear and non-Gaussian environments that are frequently encountered in real-world scenarios, we thus propose the approximated kernel least mean p-power (AKLM-P) algorithm to address these tough factors for nonlinear distributed systems. In order to efficiently devise this algorithm, we use the property of shift-invariant kernel function that can be approximated by random Fourier features, and consider the impulsive noise characterized by the α-stable distribution. The stability in the mean of proposed distributed algorithm is then studied. Finally, the superiorities of proposed approach compared to diffusion kernelleast-mean-square (KLMS) algorithm are confirmed via the experimental results.

AB - Considering nonlinear and non-Gaussian environments that are frequently encountered in real-world scenarios, we thus propose the approximated kernel least mean p-power (AKLM-P) algorithm to address these tough factors for nonlinear distributed systems. In order to efficiently devise this algorithm, we use the property of shift-invariant kernel function that can be approximated by random Fourier features, and consider the impulsive noise characterized by the α-stable distribution. The stability in the mean of proposed distributed algorithm is then studied. Finally, the superiorities of proposed approach compared to diffusion kernelleast-mean-square (KLMS) algorithm are confirmed via the experimental results.

KW - Distributed diffusion

KW - Impulsive noise

KW - Kernel least mean p-power

KW - Random Fourier features

KW - Stability analysis

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DO - 10.1109/ICSPCC46631.2019.8960839

M3 - 会议稿件

AN - SCOPUS:85078924269

T3 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

BT - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Y2 - 20 September 2019 through 22 September 2019

ER -

Gao W, Chen J, Zhang L. Diffusion approximated kernel least mean P-power algorithm. In 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8960839. (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019). doi: 10.1109/ICSPCC46631.2019.8960839

Diffusion approximated kernel least mean P-power algorithm

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