| 1 |
Blind Learning of Tree Network Topologies in the Presence of Hidden Nodes
Sepehr F, Materassi D
IEEE Transactions on Automatic Control, 65(3), 1014, 2020 |
| 2 |
Learning Latent Variable Dynamic Graphical Models by Confidence Sets Selection
Ciccone V, Ferrante A, Zorzi M
IEEE Transactions on Automatic Control, 65(12), 5130, 2020 |
| 3 |
Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting
Wang ED, Alp N, Shi J, Wang C, Zhang XD, Chen H
Energy, 125, 197, 2017 |
| 4 |
Semi-supervised PLVR models for process monitoring with unequal sample sizes of process variables and quality variables
Zhou L, Chen JH, Song ZH, Ge ZQ
Journal of Process Control, 26, 1, 2015 |
| 5 |
On-line sequential extreme learning machine based on recursive partial least squares
Matias T, Souza F, Araujo R, Goncalves N, Barreto JP
Journal of Process Control, 27, 15, 2015 |
| 6 |
Probabilistic latent variable regression model for process-quality monitoring
Zhou L, Chen JH, Song ZH, Ge ZQ, Miao AM
Chemical Engineering Science, 116, 296, 2014 |
| 7 |
Robust Processes Through Latent Variable Modeling and Optimization
Yacoub F, MacGregor JF
AIChE Journal, 57(5), 1278, 2011 |
| 8 |
New approach for microkinetic mean-field modelling using latent variables
Sjoblom J, Creaser D
Computers & Chemical Engineering, 31(4), 307, 2007 |
| 9 |
A behavioral approach to time-varying linear systems. Part 1: General theory
Ilchmann A, Mehrmann V
SIAM Journal on Control and Optimization, 44(5), 1725, 2005 |
