A robust extreme learning machine for modeling a small-scale turbojet engine
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DOI: 10.1016/j.apenergy.2018.02.175
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Cited by:
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- Feng, Hailong & Liu, Bei & Xu, Maojun & Li, Ming & Song, Zhiping, 2024. "Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient," Energy, Elsevier, vol. 292(C).
- 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.
- 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.
- Tang, Ruoli & Lin, Qiao & Zhou, Jinxiang & Zhang, Shangyu & Lai, Jingang & Li, Xin & Dong, Zhengcheng, 2020. "Suppression strategy of short-term and long-term environmental disturbances for maritime photovoltaic system," Applied Energy, Elsevier, vol. 259(C).
- 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).
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Keywords
Extreme learning machine; Small-scale turbojet engine; System modeling; Machine learning;All these keywords.
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