| 1 |
Recursive constrained state estimation using modified extended Kalman filter
Prakash J, Huang B, Shah SL
Computers & Chemical Engineering, 65, 9, 2014 |
| 2 |
Dynamic bayesian approach to gross error detection and compensation with application toward an oil sands process
Gonzalez R, Huang B, Xu FW, Espejo A
Chemical Engineering Science, 67(1), 44, 2012 |
| 3 |
A New Framework for Data Reconciliation and Measurement Bias Identification in Generalized Linear Dynamic Systems
Xu H, Rong G
AIChE Journal, 56(7), 1787, 2010 |
| 4 |
Recursive state estimation techniques for nonlinear differential algebraic systems
Mandela RK, Rengaswamy R, Narasimhan S, Sridhar LN
Chemical Engineering Science, 65(16), 4548, 2010 |
| 5 |
Simultaneous robust data reconciliation and gross error detection through particle swarm optimiztion for an industrial polypropylene reactor
Prata DM, Schwaab M, Lima EL, Pinto JC
Chemical Engineering Science, 65(17), 4943, 2010 |
| 6 |
Autoassociative neural networks for robust dynamic data reconciliation
Bai SH, McLean DD, Thibault J
AIChE Journal, 53(2), 438, 2007 |
| 7 |
Enhancing dynamic data reconciliation performance through time delays identification
Yelamos I, Mendez C, Puigjaner L
Chemical Engineering and Processing, 46(12), 1251, 2007 |
| 8 |
A novel robust nonlinear dynamic data reconciliation
Gao Q, Yan WW, Shao HH
Chinese Journal of Chemical Engineering, 15(5), 698, 2007 |
| 9 |
Impact of model structure on the performance of dynamic data reconciliation
Bai SH, McLean DD, Thibault J
Computers & Chemical Engineering, 31(3), 127, 2007 |
| 10 |
Dynamic data reconciliation: Alternative to Kalman filter
Bai SH, Thibault J, McLean DD
Journal of Process Control, 16(5), 485, 2006 |
