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A robust extreme learning machine for modeling a small-scale turbojet engine

Author

Listed:
  • Zhao, Yong-Ping
  • Hu, Qian-Kun
  • Xu, Jian-Guo
  • Li, Bing
  • Huang, Gong
  • Pan, Ying-Ting

Abstract

In this paper, a robust extreme learning machine is proposed. In comparison with the original extreme learning machine and the regularized extreme learning machine, this robust algorithm minimizes both the mean and variance of modeling errors in the objective function to overcome the bias-variance dilemma. As a result, its generalization performance and robustness are enhanced, and these merits are further proved theoretically. In addition, this proposed algorithm can keep the same computational efficiency as the original extreme learning machine and the regularized extreme learning machine. Then, several benchmark data sets are used to test the effectiveness and soundness of the proposed algorithm. Finally, it is employed to model a real small-scale turbojet engine. This engine is fit well. Especially, on the idle phase, where the signal-to-noise ratio is low and it is very hard to model, the proposed algorithm performs well and its robustness is sufficiently showcased. All in all, the proposed algorithm provides a candidate technique for modeling real systems.

Suggested Citation

  • Zhao, Yong-Ping & Hu, Qian-Kun & Xu, Jian-Guo & Li, Bing & Huang, Gong & Pan, Ying-Ting, 2018. "A robust extreme learning machine for modeling a small-scale turbojet engine," Applied Energy, Elsevier, vol. 218(C), pages 22-35.
    • Handle: RePEc:eee:appene:v:218:y:2018:i:c:p:22-35
    DOI: 10.1016/j.apenergy.2018.02.175
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    References listed on IDEAS

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    Cited by:

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    2. Zhao, Hang & Liao, Zengbu & Liu, Jinxin & Li, Ming & Liu, Wei & Wang, Lei & Song, Zhiping, 2022. "A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine," Energy, Elsevier, vol. 245(C).
    3. Rui Yang & Yongbao Liu & Xing He & Zhimeng Liu, 2022. "Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor," Energies, MDPI, vol. 16(1), pages 1-19, December.
    4. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    5. Liu, Hui & Duan, Zhu & Li, Yanfei & Lu, Haibo, 2018. "A novel ensemble model of different mother wavelets for wind speed multi-step forecasting," Applied Energy, Elsevier, vol. 228(C), pages 1783-1800.

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