Lab Director | Anhui Polytechnic University | China
Professor Hongjian Liu is a distinguished academic serving as Professor at the School of Mathematics and Physics, Anhui Polytechnic University, and Director of the International Joint Research Center for Advanced Control of High-end Equipment in Anhui Province, China. He earned his Ph.D. in Control Theory and Control Engineering from Donghua University, a Master’s degree in Measurement Technique and Automation Equipment from Anhui Polytechnic University, and a Bachelor’s degree in Mathematics and Applied Mathematics from Anhui University. With a strong foundation in mathematics and engineering, Professor Liu has contributed extensively to the advancement of control theory and its applications, focusing on event-triggered state estimation, stochastic nonlinear systems, complex networks, state-dependent coefficient networks, neural networks, and memristive neural networks. His academic career reflects leadership in teaching, research supervision, and collaborative innovation, complemented by active service as a reviewer for leading international journals, including IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing. Professor Liu’s research excellence is evidenced by his prolific scholarly output and significant global influence in systems science and automation. His research impact metrics include 4,769 citations, 142 publications, and an h-index of 41, underscoring his outstanding academic achievements and sustained contributions to the scientific community.
Profiles: Scopus | Google Scholar
Featured Publications
Hu, J., Zhang, H., Liu, H., & Yu, X. (2021). A survey on sliding mode control for networked control systems. International Journal of Systems Science, 52(6), 1129–1147.
Mao, J., Sun, Y., Yi, X., Liu, H., & Ding, D. (2021). Recursive filtering of networked nonlinear systems: A survey. International Journal of Systems Science, 52(6), 1110–1128.
Ju, Y., Tian, X., Liu, H., & Ma, L. (2021). Fault detection of networked dynamical systems: A survey of trends and techniques. International Journal of Systems Science, 52(16), 3390–3409.
Liu, H., Wang, Z., Shen, B., & Liu, X. (2017). Event-triggered state estimation for delayed stochastic memristive neural networks with missing measurements: The discrete time case. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3726–3737.
Shen, B., Wang, Z., Wang, D., & Liu, H. (2019). Distributed state-saturated recursive filtering over sensor networks under round-robin protocol. IEEE Transactions on Cybernetics, 50(8), 3605–3615.