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

GU Jun; ZOU Quanyi; DENG Changhui; WANG Xiaojun

doi:10.23919/cje.2021.00.122

Volume 32 Issue 1

Jan. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(1): 130-139

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, 2023, 32(1): 130-139. doi: 10.23919/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, 2023, 32(1): 130-139. doi: 10.23919/cje.2021.00.122

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

doi: 10.23919/cje.2021.00.122

1.
College of Information Engineering, Dalian Ocean University, Dalian 116023, China
2.
School of software Engineering, South China University of Technology, Guang Zhou 510006, China
3.
School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, China

Funds: This work was supported by Scientific Research Funding Project of the Education Department of Liaoning Province (LN2019Q44)

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Author Bio:
Jun GU is a Senior Experimenter in Telecommunications Experiment Center, School of Information Engineering, Dalian Ocean University. Her research interests include electrical and electronic technology and electrical automation

Quanyi ZOU is pursuing the Ph.D. degree in the School of Software Engineering, South China University of Technology, Guangzhou, China. His current research interests include machine learning, transfer learning, and software defect prediction

Changhui DENG is a Professor at Dalian Ocean University, Dalian, China. His research interests include complex process modeling and control, artificial intelligence technology, and intelligent detection

Xiaojun WANG (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 and 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. (Email: wxjjessicaxj0903@126.com)
Received Date: 2021-04-09
Accepted Date: 2021-12-08

Available Online: 2021-12-29

Publish Date: 2023-01-05

Abstract

Abstract

Samples collected from most industrial processes have two challenges: one is contaminated by the non-Gaussian noise, and the other is gradually obsolesced. This feature 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 which the least mean $\boldsymbol{p}$-power 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 online sequential ELM (OS-ELM) and the OS-ELM with forgetting mechanism (FOS-ELM). The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.
- Extreme learning machine (ELM),
- Online learning,
- Non-Gaussian noise,
- Obsolescence samples,
- Least mean $\boldsymbol{p}$-power (LMP)

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References(20)

References

[1]	B. Zou, X. Shan, C. Zhu, et al., “Deep learning and its application in diabetic retinopathy screening,” Chinese Journal of Electronics, vol.29, no.6, pp.992–1000, 2020. doi: 10.1049/cje.2020.09.001
[2]	Y. Yang, Q. M. J. Wu, X. Feng, et al., “Recomputation of the dense layers for performance improvement of DCNN,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.42, no.11, pp.2912–2925, 2020. doi: 10.1109/TPAMI.2019.2917685
[3]	X. Yuan, P. He, Q. Zhu, et al., “Adversarial examples: Attacks and defenses for deep learning,” IEEE Transactions on Neural Networks and Learning Systems, vol.30, no.9, pp.2805–2824, 2019. doi: 10.1109/TNNLS.2018.2886017
[4]	G. Cai, G. Lyu, Y. Lin, et al., “Multi-level deep correlative networks for multi-modal sentiment analysis,” Chinese Journal of Electronics, vol.29, no.6, pp.1025–1038, 2020. doi: 10.1049/cje.2020.09.003
[5]	Y. Zhang, K. Liu, Q. Zhang, et al., “A combined-convolutional neural network for Chinese news text classification,” CTA Electonica Sinica, vol.49, no.6, pp.1059–1067, 2021.
[6]	G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol.70, no.1-3, pp.489–501, 2006. doi: 10.1016/j.neucom.2005.12.126
[7]	G. An, Z. Jiang, X. Cao, et al., “Short-term wind power prediction based on particle swarm optimization-extreme learning machine model combined with Adaboost algorithm,” IEEE Access, vol.9, pp.94040–94052, 2021. doi: 10.1109/ACCESS.2021.3093646
[8]	J. Zhao, W. Wang, Q. Sun, et al., “CSELM-QE: A composite semi-supervised extreme learning machine with unlabeled RSS quality estimation for radio map construction,” Chinese Journal of Electronics, vol.29, no.6, pp.1016–1024, 2020. doi: 10.1049/cje.2020.09.002
[9]	F. Golestaneh, P. Pierre, and H. B. Gooi, “Very short-term nonparametric probabilistic forecasting of renewable energy generation-with application to solar energy,” IEEE Transactions on Power Systems, vol.31, no.5, pp.3850–3863, 2016. doi: 10.1109/TPWRS.2015.2502423
[10]	N. Liang, G. B. Huang, P. Saratchandran, et al., “A fast and accurate online sequential learning algorithm for feed-forward networks,” IEEE Transactions on Neural Networks, vol.17, no.6, pp.1411–1423, 2006. doi: 10.1109/TNN.2006.880583
[11]	J. Zhao, Z. Wang, and D. S. Park, “Online sequential extreme learning machine with forgetting mechanism,” Neurocomputing, vol.87, pp.79–89, 2012. doi: 10.1016/j.neucom.2012.02.003
[12]	Q. Y. Zou, X. J. Wang, C. J. Zhou, et al., “The memory degradation based online sequential extreme learning machine,” Neurocomputing, vol.275, pp.2864–2879, 2018. doi: 10.1016/j.neucom.2017.11.030
[13]	J. Yang, F. Ye, H. J. Rong, et al., “Recursive least mean p-power extreme learning machine,” Neural Networks, vol.91, pp.22–33, 2017. doi: 10.1016/j.neunet.2017.04.001
[14]	S. M. Jung and P. G. Park, “Stabilization of a bias-compensated normalized least mean square algorithm for noisy inputs,” IEEE Transactions on Signal Processing, vol.65, no.11, pp.2949–2961, 2017. doi: 10.1109/TSP.2017.2675865
[15]	B. Chen, L. Xing, Z. Wu, et al., “Smoothed least mean p-power error criterion for adaptive filtering,” Digital Signal Processing, vol.40, pp.154–163, 2015. doi: 10.1016/j.dsp.2015.02.009
[16]	S. C. Pei and C. C. Tseng, “Least mean p-power error criterion for adaptive FIR flter,” IEEE Journal on Selected Areas in Communications, vol.12, no.9, pp.1540–1547, 1994. doi: 10.1109/49.339922
[17]	H. Sadreazami, M. O. Ahmad, and M. N. S. Swamy, “Despeckling of synthetic aperture radar images in the contourlet domain using the alpha-stable distribution,” in Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal, pp.121–124, 2015.
[18]	Y. Chen, “Suboptimum detectors for AF relaying with gaussian noise and interference,” IEEE Transactions on Vehicular Technology, vol.64, no.10, pp.4833–4839, 2015. doi: 10.1109/TVT.2014.2372040
[19]	J. IIow, D. Hatzinakos, and A. N. Venetsanopoulos, “Performance of FHSS radio networks with interference modeled as a mixture of Gaussian and alpha-stable noise,” IEEE Transactions on Communications, vol.46, no.4, pp.509–520, 1998. doi: 10.1109/26.664307
[20]	R. D. Pierce, “Application of the positive alpha-stable distribution,” in Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics (HOST), Banff, AB, Canada, pp.420–424, 1997.