| 1 |
Monitoring of wastewater treatment processes using dynamic concurrent kernel partial least squares
Liu HB, Yang J, Zhang YC, Yang C
Process Safety and Environmental Protection, 147, 274, 2021 |
| 2 |
Monitoring of quality-relevant and quality-irrelevant blocks with characteristic-similar variables based on self-organizing map and kernel approaches
Yan SF, Huang JP, Yan XF
Journal of Process Control, 73, 103, 2019 |
| 3 |
A feature-based soft sensor for spectroscopic data analysis
Shah D, Wang J, He QP
Journal of Process Control, 78, 98, 2019 |
| 4 |
Locally weighted kernel partial least squares regression based on sparse nonlinear features for virtual sensing of nonlinear time-varying processes
Zhang XM, Kano M, Li Y
Computers & Chemical Engineering, 104, 164, 2017 |
| 5 |
Quality-related fault detection approach based on dynamic kernel partial least squares
Jia QL, Zhang YW
Chemical Engineering Research & Design, 106, 242, 2016 |
| 6 |
Multivariate data modeling using modified kernel partial least squares
Gao YB, Kong XY, Hu CH, Zhang ZX, Li HZ, Hou LA
Chemical Engineering Research & Design, 94, 466, 2015 |
| 7 |
Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills
Tang J, Chai TY, Liu Z, Yu W
Chinese Journal of Chemical Engineering, 23(12), 2020, 2015 |
| 8 |
Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes
Wang L, Shi HB
Chinese Journal of Chemical Engineering, 22(6), 657, 2014 |
| 9 |
Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes
Jin HP, Chen XG, Yang JW, Wu L
Computers & Chemical Engineering, 71, 77, 2014 |
| 10 |
On-line batch process monitoring using hierarchical kernel partial least squares
Zhang YW, Hu ZY
Chemical Engineering Research & Design, 89(10A), 2078, 2011 |
