A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise-Reference-Cited by-同舟云学术

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A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise

Published:2023-01 Issue:1 Volume:32 Page:130-139
ISSN:2075-5597
Container-title:Chinese Journal of Electronics
language:
Short-container-title:Chinese J. Elect.

Author:

Gu Jun¹,Zou Quanyi²,Deng Changhui¹,Wang Xiaojun³

Affiliation:

1. College of Information Engineering, Dalian Ocean University,Dalian,China,116023

2. School of software Engineering, South China University of Technology,Guang Zhou,China,510006

3. School of Management Science and Engineering, Dongbei University of Finance and Economics,Dalian,China,116025

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/9970761/10038784/10038815.pdf?arnumber=10038815

Reference20 articles.

1. Recursive least meanp-power Extreme Learning Machine

2. The memory degradation based online sequential extreme learning machine

3. Smoothed least mean p-power error criterion for adaptive filtering

4. Stabilization of a Bias-Compensated Normalized Least-Mean-Square Algorithm for Noisy Inputs

5. Application of the positive alpha-stable distribution

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1. A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography;Applied Sciences;2024-01-10

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