| 1 |
Latent variable iterative learning model predictive control for multivariable control of batch processes
Li XW, Zhao ZG, Liu F
Journal of Process Control, 94, 1, 2020 |
| 2 |
Monitoring and prediction of big process data with deep latent variable models and parallel computing
Yang ZY, Ge ZQ
Journal of Process Control, 92, 19, 2020 |
| 3 |
Advances and opportunities in machine learning for process data analytics
Qin SJ, Chiang LH
Computers & Chemical Engineering, 126, 465, 2019 |
| 4 |
A review of the Expectation Maximization algorithm in data-driven process identification
Sammaknejad N, Zhao YJ, Huang B
Journal of Process Control, 73, 123, 2019 |
| 5 |
Extracting dynamic features with switching models for process data analytics and application in soft sensing
Ma YJ, Huang B
AIChE Journal, 64(6), 2037, 2018 |
| 6 |
Process monitoring using a generalized probabilistic linear latent variable model
Raveendran R, Kodamana H, Huang B
Automatica, 96, 73, 2018 |
| 7 |
Dynamic latent variable analytics for process operations and control
Dong YN, Qin SJ
Computers & Chemical Engineering, 114, 69, 2018 |
| 8 |
Uncertainty back-propagation in PLS model inversion for design space determination in pharmaceutical product development
Bano G, Facco P, Meneghetti N, Bezzo F, Barolo M
Computers & Chemical Engineering, 101, 110, 2017 |
| 9 |
Constrained latent variable model predictive control for trajectory tracking and economic optimization in batch processes
Godoy JL, Gonzalez AH, Normey-Rico JE
Journal of Process Control, 45, 1, 2016 |
| 10 |
A Bayesian Framework for Real-Time Identification of Locally Weighted Partial Least Squares
Ma M, Khatibisepehr S, Huang B
AIChE Journal, 61(2), 518, 2015 |
