| 1 |
Incipient fault diagnosis for centrifugal chillers using kernel entropycomponent analysis and voting based extreme learning machine
Xia Y, Ding Q, Jiang A, Jing N, Zhoug W, Wang J
Korean Journal of Chemical Engineering, 39(3), 504, 2022 |
| 2 |
Color difference classification of dyed fabrics via a kernel extreme learning machine based on an improved grasshopper optimization algorithm
Li JQ, Shi WM, Yang DH
Color Research and Application, 46(2), 388, 2021 |
| 3 |
State of health prediction for lithium-ion batteries with a novel online sequential extreme learning machine method
Tian HX, Qin PL
International Journal of Energy Research, 45(2), 2383, 2021 |
| 4 |
Multi-step wind speed forecast based on sample clustering and an optimized hybrid system
Chen XJ, Zhao J, Jia XZ, Li ZL
Renewable Energy, 165, 595, 2021 |
| 5 |
A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM
Fu WL, Zhang K, Wang K, Wen B, Fang P, Zou F
Renewable Energy, 164, 211, 2021 |
| 6 |
Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane
Guan K, Gao Y, Zeng QF, Luan XG, Zhang Y, Cheng LF, Wu JQ, Lu ZY
Chinese Journal of Chemical Engineering, 28(6), 1733, 2020 |
| 7 |
Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine
Liu HB, Zhang YC, Zhang H
Process Biochemistry, 97, 72, 2020 |
| 8 |
Ultrasound-assisted process optimization and tribological characteristics of biodiesel from palm-sesame oil via response surface methodology and extreme learning machine - Cuckoo search
Mujtaba MA, Masjuki HH, Kalam MA, Ong HC, Gul M, Farooq M, Soudagar MEM, Ahmed W, Harith MH, Yusoff MNAM
Renewable Energy, 158, 202, 2020 |
| 9 |
Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting
Peng T, Zhang C, Zhou JZ, Nazir MS
Renewable Energy, 156, 804, 2020 |
| 10 |
A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting
Hao Y, Tian CS
Applied Energy, 238, 368, 2019 |
