Dynamic R-parameter based integrated model predictive iterative learning control for batch processes L Jia, C Han, M Chiu Journal of Process Control 49, 26-35, 2017 | 53 | 2017 |
Neuro-fuzzy based identification method for Hammerstein output error model with colored noise F Li, L Jia, D Peng, C Han Neurocomputing 244, 90-101, 2017 | 37 | 2017 |
Model predictive control of batch processes based on two-dimensional integration frame C Han, L Jia, D Peng Nonlinear Analysis: Hybrid Systems 28, 75-86, 2018 | 34 | 2018 |
An integrated model predictive control strategy for batch processes L Jia, C Han, M Chiu 2016 Chinese Control and Decision Conference (CCDC), 5802-5807, 2016 | 5 | 2016 |
A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware N Vanattou-Saïfoudine, C Han, R Krause, E Vasilaki, W von der Behrens, ... Scientific Reports 11 (1), 17904, 2021 | 4 | 2021 |
Modelling novelty detection in the thalamocortical loop C Han, G English, HP Saal, G Indiveri, A Gilra, W von der Behrens, ... PLOS Computational Biology 19 (5), e1009616, 2023 | 3 | 2023 |
An Integrated Model Predictive Iterative Learning Control Strategy for Batch Processes C Han, L Jia Theory, Methodology, Tools and Applications for Modeling and Simulation of …, 2016 | 3 | 2016 |
Modelling the cortical representation of infrequent stimuli C Han University of Sheffield, 2022 | | 2022 |