| 1 |
RBFNN-Based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems
Yin X, Zhang QC, Wang H, Ding ZT
IEEE Transactions on Automatic Control, 65(1), 376, 2020 |
| 2 |
Learning based personalized energy management systems for residential buildings
Soudari M, Srinivasan S, Balasubramanian S, Vain J, Kotta U
Energy and Buildings, 127, 953, 2016 |
| 3 |
Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)
Cheng SJ, Miao JM, Wu SJ
Applied Energy, 105, 161, 2013 |
| 4 |
Short-term solar power prediction using a support vector machine
Zeng JW, Qiao W
Renewable Energy, 52, 118, 2013 |
| 5 |
Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches
Elsayed K, Lacor C
Powder Technology, 212(1), 115, 2011 |
| 6 |
Adaptive online state-of-charge determination based on neuro-controller and neural network
Shen YQ
Energy Conversion and Management, 51(5), 1093, 2010 |
| 7 |
Prediction of hydrophile-lipophile balance values of anionic surfactants using a quantitative structure-property relationship
Luan F, Liu HT, Gao YA, Li QZ, Zhang XY, Guo Y
Journal of Colloid and Interface Science, 336(2), 773, 2009 |
| 8 |
Nonlinear dynamic modeling for a SOFC stack by using a Hammerstein model
Huo HB, Zhong ZD, Zhu XH, Tu HY
Journal of Power Sources, 175(1), 441, 2008 |
| 9 |
Nonlinear model predictive control of SOFC based on a Hammerstein model
Huo HB, Zhu XJ, Hu WQ, Tu HY, Li J, Yang J
Journal of Power Sources, 185(1), 338, 2008 |
| 10 |
Nonlinear modeling of a SOFC stack based on a least squares support vector machine
Huo HB, Zhu XH, Cao GY
Journal of Power Sources, 162(2), 1220, 2006 |
