| 1 |
Fault diagnosis of nonlinear systems using recurrent neural networks
Shahnazari H
Chemical Engineering Research & Design, 153, 233, 2020 |
| 2 |
Decentralized machine-learning-based predictive control of nonlinear processes
Chen S, Wu Z, Christofides PD
Chemical Engineering Research & Design, 162, 45, 2020 |
| 3 |
Integrating dynamic neural network models with principal component analysis for adaptive model predictive control
Hassanpour H, Corbett B, Mhaskar P
Chemical Engineering Research & Design, 161, 26, 2020 |
| 4 |
Online state of health prediction method for lithium-ion batteries, based on gated recurrent unit neural networks
Ungurean L, Micea MV, Carstoiu G
International Journal of Energy Research, 44(8), 6767, 2020 |
| 5 |
Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes
Wu Z, Rincon D, Christofides PD
Journal of Process Control, 89, 74, 2020 |
| 6 |
Assessment of deep recurrent neural network-based strategies for short-term building energy predictions
Fan C, Wang JY, Gang WJ, Li SH
Applied Energy, 236, 700, 2019 |
| 7 |
Modeling and fault diagnosis design for HVAC systems using recurrent neural networks
Shahnazari H, Mhaskar P, House JM, Salsbury TI
Computers & Chemical Engineering, 126, 189, 2019 |
| 8 |
Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks
Liu JW, Li Q, Chen WR, Yan Y, Qiu YB, Cao TQ
International Journal of Hydrogen Energy, 44(11), 5470, 2019 |
| 9 |
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
Rahman A, Srikumar V, Smith AD
Applied Energy, 212, 372, 2018 |
| 10 |
Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms
Rahman A, Smith AD
Applied Energy, 228, 108, 2018 |
