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Zejian Zhou

Zejian Zhou


Assistant Professor
EN 5036
zzhou2@uwyo.edu

Education

  • Ph.D., Electrical Engineering, University of Nevada, Reno, NV
  • M.E., Electrical Engineering, Stevens Institute of Technology, Hoboken, NJ

Area of Expertise

Dr. Zejian Zhou’s research interests include game theory, multi-agent decision-making, control systems, deep reinforcement learning, and robotics.

Zejian Zhou headshot

Selected Publications

Selected journal papers

  • Pednekar, D. D., Liguori, M. A., Marques, C. N., Zhang, T., Zhang, N., Zhou, Z., Amoako, K. & Gu, H. (2022). From Static to Dynamic: A Review on the Role of Mucus Heterogeneity in Particle and Microbial Transport. ACS Biomaterials Science & Engineering, 8(7), 2825-2848.
  • Zhou Z., & Xu H.*, Decentralized Adaptive Optimal Tracking Control for Massive Autonomous Vehicle Systems with Heterogeneous Dynamics: A Stackelberg Game, IEEE Transactions on Neural Networks and Learning Systems (IF=10.46) (in press)
  • Zhou Z., & Xu H.*, Decentralized Adaptive Optimal Tracking Control for Massive Multi-agent Systems: An Actor-Critic-Mass Algorithm, IEEE Transactions on Neural Networks and Learning Systems (IF=10.46) (in press)
  • Zhou Z., & Xu H.*, (2020) A Novel Mean-Field-Game-Type Optimal Control for Very Large-Scale Multiagent Systems, IEEE Transactions on Cybernetics (IF=11.448) (in press)
  • Zhou Z., & Xu H.*, Decentralized Optimal Large Scale Multi-Player Pursuit-evasion Strategies: A Mean Field Game Approach with Reinforcement Learning, Neurocomputing (JCR Q1) (in press)
  • Zhou, Z.*, Xiang, Y., Xu, H., Yi, Z., Shi, D., & Wang, Z. (2020), A Novel Transfer Learning based Intelligent Non-intrusive Load Monitoring with Limited Measurements. IEEE Transactions on Instrumentation and Measurement. (JCR Q1)
  • Zhou Z.*, Xiang, Y., Xu, H., Wang, Y., & Shi, D. Unsupervised Learning for Non-Intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network, Journal of Modern Power Systems and Clean Energy (JCR Q1) (in press)
  • Zhou, Z.*, Xiang, Y., Xu, H., Wang, Y., Shi, D., & Wang, Z. (2020). Self-organizing probability neural network-based intelligent non-intrusive load monitoring with applications to low-cost residential measuring devices. Transactions of the Institute of Measurement and Control.

Peer-reviewed conference papers

  • Zhou, Z., Zhang, Y., & Xu, H. (2021, July). Reinforcement Learning-based Decentralized Optimal Control for Large-Scale Multi-agent System by Using Neural Networks and Discrete-time Mean Field Games. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE. (link)
  • Zhou, Z., & Xu, H. (2021, August). Decentralized Optimal Tracking Control for Large-scale Multi-Agent Systems under Complex Environment: A Constrained Mean Field Game with Reinforcement Learning Approach. In 2021 IEEE Conference on Control Technology and Applications (CCTA) (pp. 111-116). IEEE. (link)
  • Zhou, Z., & Xu, H. (2021, August). Decentralized Optimal Multi-agent System Tracking Control Using Mean Field Games with Heterogeneous Agent. In 2021 IEEE Conference on Control Technology and Applications (CCTA) (pp. 97-102). IEEE. (link)
  • Zhou, Z., & Xu, H. (2020, July). Mean Field Game and Decentralized Intelligent Adaptive Pursuit Evasion Strategy for Massive Multi-Agent System under Uncertain Environment. In 2020 American Control Conference (ACC) (pp. 5382-5387). IEEE. (link)
  • Zhou, Z., Fadali, S., & Xu, H. (2020, July). Biomimetic Optimal Tracking Control using Mean Field Games and Spiking Neural Networks. In 2020 International Federation of Automatic Control World Congress, IFAC. (link)
  • Zhou, Z., & Xu, H.(2020, October). Biologically Inspired Decentralized Adaptive Optimal Tracking Control for Large Scale Multi-Agent Systems with Input Constraint. In 16th IEEE International Conference on Control and Automation (ICCA 2020). (link)
  • Zhou, Z., & Xu, H. (2019, December). Decentralized Adaptive Optimal Tracking Control for Massive Multi-agent Systems: An Actor-Critic-Mass Algorithm. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 1231-1236). IEEE. (link)
  • Zhou, Z., & Xu, H. (2019, December). Decentralized Adaptive Optimal Control for Massive Multi-agent Systems Using Mean Field Game with Self-Organizing Neural Networks. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 1225-1230). IEEE. (link)
  • Zhou, Z., & Xu, H. (2019, December). Decentralized Adaptive Optimal Tracking Control for Massive Multi-Agent Systems with Input Constraint. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE. (link)
  • Zhou, Z., & Xu, H. (2019, December). Deep Reinforcement Learning Based Intelligent Decision-making for Two-player Sequential Game with Uncertain Irrational Player. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 9-15). IEEE. (link)
  • Zhou, Z., Qian, L., & Xu, H. (2019, November). Intelligent Decentralized Dynamic Power Allocation in MANET at Tactical Edge based on Mean-Field Game Theory. In MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM) (pp. 604-609). IEEE. (link)
  • Wu, H. H., Zhou, Z., Feng, M., Yan, Y., Xu, H., & Qian, L. (2019, June). Real-time single object detection on the uav. In 2019 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1013-1022). IEEE. (link)
  • Zhou, Z., & Xu, H. (2018, November). Switching deep reinforcement learning based intelligent online decision-making for autonomous systems under uncertain environment. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1453-1460). IEEE. (link)
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Honors and Awards

  • Graduate Dean’s Merit Scholarships, University of Nevada, Reno, 2020
  • Provost's Master’s Fellowship Award, Stevens Institute of Technology, 2016
  • Travel Grants Award, University of Nevada, Reno, 2018, 2019
 
 
 
 
 
 






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