| Linear mapping approximation of gene regulatory networks with stochastic dynamics Z Cao, R Grima Nature Communications 9, 2018 | 150 | 2018 |
| Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells Z Cao, R Grima Proceedings of the National Academy of Sciences 117 (9), 4682-4692, 2020 | 146 | 2020 |
| Neural network aided approximation and parameter inference of non-Markovian models of gene expression Q Jiang, X Fu, S Yan, R Li, W Du, Z Cao, F Qian, R Grima Nature communications 12 (1), 2618, 2021 | 104 | 2021 |
| Nonlinear Monotonically Convergent Iterative Learning Control for Batch Processes J Lu, Z Cao, R Zhang, F Gao IEEE Transactions on Industrial Electronics 65 (7), 5826 - 5836, 2017 | 91 | 2017 |
| Multipoint iterative learning model predictive control J Lu, Z Cao, F Gao IEEE Transactions on Industrial Electronics 66 (8), 6230-6240, 2018 | 66 | 2018 |
| A Systematic Min-Max Optimization Design of Constrained Model Predictive Tracking Control for Industrial Processes against Uncertainty R Zhang, S Wu, Z Cao, J Lu, F Gao IEEE Transactions on Control Systems Technology, 2017 | 56 | 2017 |
| Design of fractional order modeling based extended non-minimal state space MPC for temperature in an industrial electric heating furnace R Zhang, Q Zou, Z Cao, F Gao Journal of Process Control 56, 13-22, 2017 | 48 | 2017 |
| A stochastic model of gene expression with polymerase recruitment and pause release Z Cao, T Filatova, DA Oyarzún, R Grima Biophysical Journal 119 (5), 1002-1014, 2020 | 47* | 2020 |
| 110th Anniversary: An Overview on Learning-Based Model Predictive Control for Batch Processes J Lu, Z Cao, C Zhao, F Gao Industrial & Engineering Chemistry Research 58 (37), 17164-17173, 2019 | 40 | 2019 |
| Constrained two dimensional recursive least squares model identification for batch processes Z Cao, Y Yang, J Lu, F Gao Journal of Process Control 24 (6), 871-879, 2014 | 40 | 2014 |
| New PID controller design using extended nonminimal state space model based predictive functional control structure R Zhang, Z Cao, C Bo, P Li, F Gao Industrial & Engineering Chemistry Research 53 (8), 3283-3292, 2014 | 39 | 2014 |
| Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data Z Cao, R Grima Journal of The Royal Society Interface 16 (153), 20180967, 2019 | 37 | 2019 |
| Iterative learning Kalman filter for repetitive processes Z Cao, J Lu, R Zhang, F Gao Journal of Process Control 46, 92-104, 2016 | 35 | 2016 |
| Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions X Fu, HP Patel, S Coppola, L Xu, Z Cao, TL Lenstra, R Grima Elife 11, e82493, 2022 | 34 | 2022 |
| Stochastic modeling of autoregulatory genetic feedback loops: A review and comparative study J Holehouse, Z Cao, R Grima Biophysical Journal 118 (7), 1517-1525, 2020 | 34 | 2020 |
| Discrete-time robust iterative learning Kalman filtering for repetitive processes Z Cao, R Zhang, Y Yang, J Lu, F Gao IEEE Transactions on Automatic Control 61 (1), 270-275, 2015 | 34 | 2015 |
| A two-stage design of two-dimensional model predictive iterative learning control for nonrepetitive disturbance attenuation J Lu, Z Cao, Z Wang, F Gao Industrial & Engineering Chemistry Research 54 (21), 5683-5689, 2015 | 32 | 2015 |
| Extremum seeking control for personalized zone adaptation in model predictive control for type 1 diabetes Z Cao, R Gondhalekar, E Dassau, FJ Doyle IEEE Transactions on Biomedical Engineering 65 (8), 1859-1870, 2017 | 31 | 2017 |
| Batch process control-overview and outlook J Lu, Z Cao, F Gao Acta Automatica Sinica 43 (6), 933-943, 2017 | 29 | 2017 |
| Iterative learning and extremum seeking for repetitive time-varying mappings Z Cao, HB Dürr, C Ebenbauer, F Allgöwer, F Gao IEEE Transactions on Automatic Control 62 (7), 3339-3353, 2016 | 28 | 2016 |
Furong GaoHong Kong University of Science and TechnologyVerified email at ust.hk
