| 1 |
A Robust Infinite Gaussian Mixture Model and Its Application in Fault Detection of Nonlinear Multimode Processes
Pan Y, Xie L, Su HY, Luo L
Journal of Chemical Engineering of Japan, 53(12), 758, 2020 |
| 2 |
Gaussian process modelling with Gaussian mixture likelihood
Daemi A, Kodamana H, Huang B
Journal of Process Control, 81, 209, 2019 |
| 3 |
Robustness analysis of a maximum correntropy framework for linear regression
Bako L
Automatica, 87, 218, 2018 |
| 4 |
Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data
Sadeghian A, Wu O, Huang B
Journal of Process Control, 67, 94, 2018 |
| 5 |
Approaches to robust process identification: A review and tutorial of probabilistic methods
Kodamana H, Huang B, Ranjan R, Zhao YJ, Tan RM, Sammaknejad N
Journal of Process Control, 66, 68, 2018 |
| 6 |
A data-driven adaptive multivariate steady state detection strategy for the evaporation process of the sodium aluminate solution
Xie S, Yang CH, Wang XL, Yuan XF, Xie YF
Journal of Process Control, 68, 145, 2018 |
| 7 |
On a Class of Optimization-Based Robust Estimators
Bako L
IEEE Transactions on Automatic Control, 62(11), 5990, 2017 |
| 8 |
Deep recurrent Gaussian processes for outlier-robust system identification
Mattos CLC, Dai ZW, Damianou A, Barreto GA, Lawrence ND
Journal of Process Control, 60, 82, 2017 |
| 9 |
Analysis of a nonsmooth optimization approach to robust estimation
Bako L, Ohlsson H
Automatica, 66, 132, 2016 |
| 10 |
Robust EM kernel-based methods for linear system identification
Bottegal G, Aravkin AY, Hjalmarsson H, Pillonetto G
Automatica, 67, 114, 2016 |
