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GU Jun, ZOU Quanyi, DENG Changhui, WANG Xiaojun. A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.122
Citation: GU Jun, ZOU Quanyi, DENG Changhui, WANG Xiaojun. A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.122

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

doi: 10.1049/cje.2021.00.122
Funds:  This work is supported by the National Natural Science Foundation of China (61702070), and Scientific Research Funding Project of the Education Department of Liaoning Province (LN2019Q44)
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  • Author Bio:

    (Corresponding author) received the B.S. degree in automation from Dalian Ocean University, Dalian, China, in 2009. She received the M.S. and Ph.D. degrees in control theory & control engineering from Northeastern University, Shenyang, China, in 2011 and 2016, respectively. She is a lecturer of School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China. Her research interests include recommender system, deep learning, machine learning, modeling of industrial processes and soft sensors. She is the first or the corresponding author of about 20 papers. (wxjjessicaxj0903@126.com)

  • Received Date: 2021-04-09
  • Accepted Date: 2021-12-08
  • Available Online: 2021-12-29
  • Samples collected from most industrial processes have two challenges: one is contaminated by the non-Gaussian noise, and the other is gradually obsolesced. These features can obviously reduce the accuracy and generalization of models. To handle these challenges, a novel method, named the robust online extreme learning machine (RO-ELM), is proposed in this paper. In the RO-ELM, the least mean $ p $-power (LMP) criterion is employed as the cost function which is to boost the robustness of the ELM, and the forgetting mechanism is introduced to discard the obsolescence samples. To investigate the performance of the RO-ELM, experiments on artificial and real-world datasets with the non-Gaussian noise are performed, and the datasets are from regression or classification problems. Results show that the RO-ELM is more robust than the ELM, the OS-ELM and the FOS-ELM. The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.
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