| 1 |
Research on TE process fault diagnosis method based on DBN and dropout
Wei YQ, Weng ZX
Canadian Journal of Chemical Engineering, 98(6), 1293, 2020 |
| 2 |
A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox
Pan YB, Hong RJ, Chen J, Wu WW
Renewable Energy, 152, 138, 2020 |
| 3 |
Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods
Cui J, Yu RZ, Zhao DB, Yang JY, Ge WC, Zhou XM
Applied Energy, 247, 480, 2019 |
| 4 |
A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing
Zhang YC, Le J, Liao XB, Zheng F, Li YH
Energy, 168, 558, 2019 |
| 5 |
Deep learning aided interval state prediction for improving cyber security in energy internet
Wang HZ, Ruan JQ, Ma ZW, Zhou B, Fu XQ, Cao GZ
Energy, 174, 1292, 2019 |
| 6 |
Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving
Guo YB, Tan ZH, Chen HX, Li GN, Wang JY, Huang RG, Liu JY, Ahmad T
Applied Energy, 225, 732, 2018 |
| 7 |
Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system
Fu GY
Energy, 148, 269, 2018 |
| 8 |
Deep belief network based k-means cluster approach for short-term wind power forecasting
Wang KJ, Qi XX, Liu HD, Song JK
Energy, 165, 840, 2018 |
| 9 |
Modeling of Boiler-Turbine Unit with Two-Phase Feature Selection and Deep Belief Network
Tang ZH, Wang Y, He YS, Wu XY, Cao SX
Journal of Chemical Engineering of Japan, 51(10), 865, 2018 |
| 10 |
A deep belief network based fault diagnosis model for complex chemical processes
Zhang ZP, Zhao JS
Computers & Chemical Engineering, 107, 395, 2017 |
