Hui Meng Professor

• Department: Electronic and Control Engineering
• Gender: male
• BirthDate:
• Career: Professor
• Degree: PHD
• Academic Credentials: Graduate
• Graduate School: Xian Jiaotong University
• Tel:
• Email: ximeng@chd.edu.cn
• Address School: ShangYuan Road
• PostCode School: 710064
• Fax School:
• Office Location: Xian, Shaanxi
• Education experience:

  2004 receive bachelor's degree in Mechanical Engineering, Xi'an Jiaotong University;

  2007 recevie master's degree in Mechanical Engineering, Xi'an Jiaotong University;

  2011 recevie PHD degree in Eletrical Engineering, Xi'an Jiaotong University;

  
  

Title：        Professor

Mobile:

E-mail:        ximeng@chd.edu.cn

Research Area:  Artificial Intelligence, Neural networks,Signal Processing.

EDUCATION

2007—2011   School of Electrical Engineering, Xi’an Jiaotong University, Xi’an

Received Ph.D. in Electrical Engineering, 2011.  

2000—2007   School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an

Received Bachelor’s and Master’s degree in Mechanical Engineering in 2004 and 2007

RESEARCH WORK

2012—present   School of Electronic and Control Engineering, Chang’an University,

Research areas: Artificial Intelligence, Electrical Engineering, Neural networks and so on.

Research Project

[1]The National Nature Science Foundation of China: Ferroresonance Characteristic Analysis, Prediction Algorithm, Power Grid Vulnerability and Disaster Spreading Model Study in Metro System (No. 51407012).

[2]The Special Fund for Basic Scientific Research of Central Colleges, Chang’an University: Research and Design of Urban Rail Transit Braking Energy Recycle System (No.310832163402).

[3]The Nature Science Foundation of Shaanxi Province: Application of memristor in analysis and suppression of ferroresonance in power system.(No. 2016JQ5047) 

Director of Shaanxi Province Instrument and Control Society

Our group conducts research on the dynamics of neural networks and complex networks. Also we apply the theoretical results to promote efficiency and resiliency in human-cyber-physical systems (HCPS), e.g., smart grid and electric transportation networks and so on.   Among other things, we have investigated the reinforcement learning, mutiagent system consensus. Our group’s research enables the practical application of the research results through the design of data-driven learning and real-time optimization algorithms, intelligent network control, synchronization of neural networks, multiagent system consensus etc.

The main research fields of our group:

1.Industry automation;

2.Deep Learning;

3.Neural Networks Dynamics.

The HCPS group of Prof. Meng Hui is looking for passionate and self-motivated Ph.D. students, master students and postdoctoral researchers.

I tend to work best with students  who are interested in interdisciplinary work. If you are interested in working with me, please apply to the CSC and Chang'an universtiy. If you would like to send me an email about your application, please include your CV and transcripts, as well as a brief description of any past research experience.

Email: ximeng@chd.edu.cn.

Artificial Intelligence

Intelligence Control

System Modelling and Simulation

Research Project

[1] The Nature Science Foundation of Shaanxi Province: The analysis of memristive neural networks and study of  synchronization control method. ( 2020JM-256)

The National Nature Science Foundation of China: Ferroresonance Characteristic Analysis, Prediction Algorithm, Power Grid Vulnerability and Disaster Spreading Model Study in Metro System (No. 51407012).

[2]The Special Fund for Basic Scientific Research of Central Colleges, Chang’an University: Research and Design of Urban Rail Transit Braking Energy Recycle System (No.310832163402).

[3]The Nature Science Foundation of Shaanxi Province: Application of memristor in analysis and suppression of ferroresonance in power system.(No. 2016JQ5047) 

PUBLICATIONS

[1] Hui, M., Zhang, J., Yao, N. et al. Finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delay and application. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-08474-4

[2] Chen Wei, XiaoPing Wang, Meng Hui and ZhiGang Zeng, Quasi-Synchronization of Fractional Multiweighted Coupled Neural Networks via Aperiodic Intermittent Control[J], IEEE Transactions on Cybernetics, 2023, doi: 10.1109/TCYB.2023.3237248.(TOP)

[3] Hui Meng, Wei C, Zhang J, et al. Finite-Time Synchronization of Fractional-Order Memristive Neural Networks via feedback and periodcally intermittent control[J]. Communications in Nonlinear Science and Numerical Simulation, 2022, 2022. (TOP)

[4] Hui Meng, Yan Jiefei, Zhang Jiahuang, et al. Exponential synchronization of inertial neural network with mixed delays via intermittent pinning control[J]. International Journal of Robust and Nonlinear Control, 2022, 32(1): 358-372.（TOP）

[5] Meng Hui, Jiahuang Zhang, Herbert Ho-Ching Iu, Rui Yao, Lin Bai, A novel intermittent sliding mode control approach to finite-time synchronization of complex-valued neural networks[J], Neurocomputing, Volume 513, 2022, Pages 181-193.（TOP）

[6] Hui Meng, Yan Jiefei. Integral Sliding Mode Exponential Synchronization of Inertial Memristive Neural Networks with Time Varying Delays[J]. Neural Processing Letter (2022). doi:10.1007/s11063-022-10981-9.  

[7] Hui Meng, Yao Ning, Iu H H C, et al. Adaptive Synchronization of Fractional-Order Complex-Valued Neural Networks With Time-Varying Delays[J]. IEEE Access, 2022, 10: 45677-45688.

[8] Hui Meng, Zhang Jiahuang, Zhang Jiao, et al. Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks With Probabilistic Time-Varying Delays[J]. IEEE ACCESS,9:44784-44796,‏. 2021

[9] Hui Meng, Wei C, Zhang J, et al. Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying Delays[J]. Complexity, 2020, 2020.

[10] Hui Meng, Luo Ni, Iu H H C, et al. Pinning Synchronization via Intermittent Control for Memristive Cohen-Grossberg Neural Networks With Mixed Delays[J]. IEEE Access, 2020, 8: 55676-55687.

[11] Meng Hui, Chen Wei, et al. Finite-Time Synchronization of Memristor-Based Fractional Order Cohen-Grossberg Neural Networks[J], IEEE Access, vol. 8, pp. 73698-73713, 2020.

[12] Hui Meng, Luo Ni, et al. New Results of Finite-Time Synchronization via Piecewise Control for Memristive Cohen-Grossberg Neural Networks With Time-Varying Delays[J]. IEEE Access, 7 (2019): 79173-79185.

[13] Zhang J, Hui M, Wei C, et al. Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Time Varying Delays[C]. 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2021: 309-314.

[14] Yan J, Hui M, Zhang J, et al. Exponential Synchronization of Switched Inertial Reaction-Diffusion Neural Networks with Time Varying Delays via Intermittent Control[C]. 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2021: 289-293.

[15] Hui M, Zhang J, Yan J, et al. Quasi-Synchronization for Complex-Valued Neural Networks with Leakage Delay and Mixed Delay[C]. 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2022: 194-199.

[16] Yao N, Hui M, Zhang J, et al. Complete Synchronization of Delayed Fractional-Order Complex-Valued Neural Networks via Adaptive Control[C]. 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2022: 173-178.

[17] Yan J, Hui M, Zhang J, et al. Integral Sliding Mode Exponential Synchronization of Inertial Neural Networks with Time Delays[C]. 2022 7th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2022: 76-80.

2012.4- Now, School of Electronic and Control Engineering, Chang'an University.