| 1 |
Nonlinear variable selection algorithms for surrogate modeling
Zhai JY, Boukouvala F
AIChE Journal, 65(8), 2019 |
| 2 |
Drivers of domestic electricity users' price responsiveness: A novel machine learning approach
Guo PY, Lam JCK, Li VOK
Applied Energy, 235, 900, 2019 |
| 3 |
Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality
Bai EW, Cheng CM, Zhao WX
Automatica, 101, 138, 2019 |
| 4 |
Ranking the importance of variables in nonlinear system identification
Cheng CM, Bai EW
Automatica, 103, 472, 2019 |
| 5 |
Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes
Pan B, Jin HP, Wang L, Qian B, Chen XG, Huang S, Li JG
Chemical Engineering Research & Design, 144, 285, 2019 |
| 6 |
Evaluation of carrier size and surface morphology in carrier-based dry powder inhalation by surrogate modeling
Farizhandi AAK, Paclawski A, Szlek J, Mendyk A, Shao YH, Lau R
Chemical Engineering Science, 193, 144, 2019 |
| 7 |
A biomimetic approach to fast selection of optimal controlled variables using multiagent algorithms and a decomposition approach
Bankole T, Bhattacharyya D, Gebreslassie B, Diwekar U
Chemical Engineering Science, 203, 475, 2019 |
| 8 |
Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model
Fan CL, Ding YF
Energy and Buildings, 197, 7, 2019 |
| 9 |
Consistent Variable Selection for a Nonparametric Nonlinear System by Inverse and Contour Regressions
Cheng CM, Bai EW, Peng ZK
IEEE Transactions on Automatic Control, 64(7), 2653, 2019 |
| 10 |
A feature-based soft sensor for spectroscopic data analysis
Shah D, Wang J, He QP
Journal of Process Control, 78, 98, 2019 |
