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Wang Leimin

Associate professor
Doctoral Supervisor
Master Tutor

Honors and Titles : Chinese Association of Automation (CAA) Outstanding Doctorate Dissertation Nomination, 2017
Gender : Male
Alma Mater : Huazhong University of Science and Technology
Education Level : Faculty of Higher Institutions
Degree : Doctoral Degree in Engineering
School/Department : School of Automation
Date of Employment : 2016-07-01
Discipline : Control Science and Engineering
Business Address : Information Building, Room 207
Contact Information : wangleimin@cug.edu.cn
Email :
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Personal Profile

Leimin Wang received the Ph.D. degree in control science and engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2016, and he is currently an Associate Professor with the School of Automation, China University of Geosciences, Wuhan, China.

He has published over 30 articles in the international top academic journals related to neural networks, artificial intelligence and automatic control, including the IEEE TNNLS, IEEE TFS, IEEE TCYB, NNs and other fields.

Leimin Wang is teaching several courses, including the undergraduate courses “Big Data Technology in Intelligent Manufacturing Process”, “Computer Networks and Industrial Internet”, and the postgraduate course "Artificial Neural Networks and Applications".


Homepage: https://www.researchgate.net/profile/Leimin_Wang

Research Interest

Neural networks, memristive systems, finite-time control, etc.

1. Theoretical direction: Dynamic analysis of memristive neural networks and memristor chaotic systems, finite-time control of nonlinear multi-agents and robot, stability and synchronization of discontinuous systems. 

2. Application direction: Optimization algorithms and applications of neural networks and multi-agent systems, Deep learning based on memristor, image encryption and associative memory.


Host Projects

1. PI, National Natural Science Foundation of China, The stability analysis and finite-time control of memristive neural networks with mixed multiple delays, 61703377, 2018.01-2020.12.

2. PI, Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), Dynamic behavior analysis and finite time control of memristor neural systems, CUG170632, 2016.09-2019.06.

Representative first author articles


1. Leimin Wang, Haibo He, and Zhigang Zeng, Intermittent stabilization of fuzzy competitive neural networks with reaction diffusions, IEEE Transactions on Fuzzy Systems, 2020, In press, doi: 10.1109/TFUZZ.2020.2999041.

2. Leimin Wang, Haibo He, and Zhigang Zeng, Global synchronization of fuzzy memristive neural networks with discrete and distributed delays, IEEE Transactions on Fuzzy Systems, 2019, In press, doi: 10.1109/TFUZZ.2019.2930032.

3. Leimin Wang, Haibo He, Zhigang Zeng, and Cheng Hu, Global stabilization of fuzzy memristor-based reaction-diffusion neural networks, IEEE Transactions on Cybernetics, 2019, In press, doi: 10.1109/TCYB.2019.2949468.

4. Leimin Wang, Haibo He, Zhigang Zeng, and Ming-Feng Ge, Model-independent formation tracking of multiple Euler-Lagrange systems via bounded inputs, IEEE Transactions on Cybernetics, 2019, In press, doi: 10.1109/TCYB.2019.2938398.

5. Leimin Wang, Zhigang Zeng, and Ming-Feng Ge. A disturbance rejection framework for finite-time and fixed-time stabilization of delayed memristive neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, doi:10.1109/TSMC.2018.2888867.

6. Leimin Wang, Mingfeng Ge, Junhao Hu, Guodong Zhang, Global stability and stabilization for inertial memristive neural networks with unbounded distributed delays, Nonlinear Dynamics, vol. 95, no. 2, pp. 943-955, 2019.

7. Leimin Wang, Tiandu Dong, Mingfeng Ge, Finite-time synchronization of memristor chaotic systems and its application in image encryption, Applied Mathematics and Computation, vol. 347, pp. 293-305, Apr. 2019. (ESI高被引论文,2019.11数据)

8. Leimin Wang, Zhigang Zeng, Xiaofeng Zong, Mingfeng Ge, Finite-time stabilization of memristor-based inertial neural networks with discontinuous activations and distributed delays, Journal of the Franklin Institute, vol. 356, no. 6, pp. 3628-3643, Apr. 2019.

9. Leimin Wang, Ming-Feng Ge, Zhigang Zeng, Junhao Hu. Finite-time robust consensus of nonlinear disturbed multiagent systems via two-layer event-triggered control, Information Sciences, vol. 466, pp. 270-283, Oct. 2018.

10. Leimin Wang, Zhigang Zeng, Ming-Feng Ge, Junhao Hu. Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays, Neural Networks, vol. 105, pp. 65-74, Sep. 2018.

11. Leimin Wang, Yi Shen, Guodong Zhang. Finite-time stabilization and adaptive control of memristor-based delayed neural networks, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2648-2659, Nov. 2017.

12. Leimin Wang, Zhigang Zeng, Junhao Hu, Xiaoping Wang. Controller design for global fixed-time synchronization of delayed neural networks with discontinuous activations, Neural Networks, vol. 87, pp. 122-131, Mar. 2017.

13. Leimin Wang, Yi Shen, Guodong Zhang. Synchronization of a class of switched neural networks with time-varying delays via nonlinear feedback control, IEEE Transactions on Cybernetics, vol. 46, no. 10, pp. 2300-2310, Oct. 2016.

14. Leimin Wang, Yi Shen, Yin Sheng. Finite-time robust stabilization of uncertain delayed neural networks with discontinuous activations via delayed feedback control, Neural Networks, vol. 76, pp. 46-54, Apr. 2016.

15. Leimin Wang, Yi Shen. Finite-time stabilizability and instabilizability of delayed memristive neural networks with nonlinear discontinuous controller, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 11, pp. 2914-2924, Nov. 2015.

16. Leimin Wang, Yi Shen, Zhixia Ding. Finite time stabilization of delayed neural networks, Neural Networks, vol. 70, pp. 74-80, Oct. 2015.

17. Leimin Wang, Yi Shen, Quan Yin, Guodong Zhang. Adaptive synchronization of memristor-based neural networks with time-varying delays, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 2033-2042, Sep. 2015. 


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