| 1 |
Deep-learning modeling and control optimization framework for intelligent thermal power plants: A practice on superheated steam temperature
Wang Q, Pan L, Lee KY, Wu Z
Korean Journal of Chemical Engineering, 38(10), 1983, 2021 |
| 2 |
A numerical investigation of direct and indirect closed-loop architectures for estimating nonminimum-phase zeros
Sobolic FM, Aljanaideh KF, Bernstein DS
International Journal of Control, 93(6), 1251, 2020 |
| 3 |
Linear prediction error methods for stochastic nonlinear models
Abdalmoaty MRH, Hjalmarsson H
Automatica, 105, 49, 2019 |
| 4 |
Machine Learning Supporting Experimental Design for Product Development in the Lab
Babutzka J, Bortz M, Dinges A, Foltin G, Hajnal D, Schultze H, Weiss H
Chemie Ingenieur Technik, 91(3), 277, 2019 |
| 5 |
Advanced wind power prediction based on data-driven error correction
Yan J, Ouyang TH
Energy Conversion and Management, 180, 302, 2019 |
| 6 |
Energy efficiency optimization of ethylene production process with respect to a novel FLPEM-based material-product nexus
Gong SX, Shao C, Zhu L
International Journal of Energy Research, 43(8), 3528, 2019 |
| 7 |
Identification of structured state-space models
Yu CP, Ljung L, Verhaegen M
Automatica, 90, 54, 2018 |
| 8 |
Prediction-error identification of LPV systems: A nonparametric Gaussian regression approach
Darwish MAH, Cox PB, Proimadis I, Pillonetto G, Toth R
Automatica, 97, 92, 2018 |
| 9 |
Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production
Ferlito S, Adinolfi G, Graditi G
Applied Energy, 205, 116, 2017 |
| 10 |
On the efficient low cost procedure for estimation of high-dimensional prediction error covariance matrices
Hoang HS, Baraille R
Automatica, 83, 317, 2017 |
