1 |
Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system Hwang JK, Yun GY, Lee S, Seo H, Santamouris M Renewable Energy, 149, 1227, 2020 |
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 |