Qizhi Wang


  1. [1] L. Zhang, S. He, J. Cheng, Z. Yuan, and X.Q. Yan, Research onneural network wind speed prediction model based on improvedsparrow algorithm optimization, Energy Reports, 8(8), 2022,739–747.
  2. [2] N. Liu, Z.Y. Su, Y.T. Chai, and S.T. Qin, Feedback neuralnetwork for constrained bi-objective convex optimization,Neurocomputing, 514, 2022, 127–136.
  3. [3] H. Liu and W. Wang, Simulation of an electronic equipmentcontrol method based on an improved neural network algorithm,Energy Reports, 8, 2022, 13409–13416.
  4. [4] A. Pan, B. Shen, and L. Wang, Ensemble of resource allocationstrategies in decision and objective spaces for multiobjectiveoptimization, Information Sciences, 605, 2022, 393–412.
  5. [5] G. Baranwal, D. Kumar, and D.P. Vidyarthi, BARA: Ablockchain-aided auction-based resource allocation in edgecomputing enabled industrial Internet of Things, FutureGeneration Computer Systems, 135, 2022, 333–347.
  6. [6] Y. Chai, G. Li, S. Qin, J. Feng, and C. Xu, A neurodynamicoptimization approach to nonconvex resource allocationproblem, Neurocomputing, 512, 2022, 178–189.
  7. [7] K. Cheng, X. Fang, and X. Wang, Optimized resource allocationand time partitioning for integrated communication, sensing,and edge computing network, Computer Communications, 194,2022, 240–249.
  8. [8] Y.-S. Chen,C.-S. Hsu, and H.-C. Hung, Optimizing commu-nication and computational resource allocations in networkslicing using twin-GAN-based DRL for 5G hybrid C-RAN,Computer Communications, 200(January), 2023, 66–85.
  9. [9] P.R. Teja and P.K. Mishra, Path selection and resourceallocation for 5G multi-hop D2D networks, ComputerCommunications, 195, 2022, 292–302.
  10. [10] Y. Zhi, J. Tian, X. Deng, J. Qiao, and D. Lu, Deep reinforcementlearning-based resource allocation for D2D communications inheterogeneous cellular networks, Digital Communications andNetworks, 8, 2021, 834–842.
  11. [11] Y. Su, Z. Gao, X. Du, and M. Guizani, User-centric base stationclustering and resource allocation for cell-edge users in 6Gultra-dense networks, Future Generation Computer Systems,141, 2022, 173–185.
  12. [12] S. Raj, Decentralized adaptive control of nonlinear intercon-nected systems, Mechatronic Systems and Control, 49(1), 2021,41–47.
  13. [13] J. Huang, F. Yang, C. Chakraborty, Z. Guo, H. Zhang, L.Zhen, and K.P. Yu, Opportunistic capacity based resourceallocation for 6G wireless systems with network slicing, FutureGeneration Computer Systems, 140, 2022, 390–401.
  14. [14] D.S. Yadav, S. Babu, and B.S. Manoj, Quasi path restoration: Apost-failure recovery scheme over pre-allocated backup resourcefor elastic optical networks, Optical Fiber Technology, 41, 2018,139–154.
  15. [15] B. Jeong, S. Baek, S. Park, J. Jeon, and Y.-S. Jeong, Stableand efficient resource management using deep neural networkon cloud computing, Neurocomputing, 521, 2022, 99–112.
  16. [16] Z. Yaghoubi and H. Zarabadipour, Hybrid neural-networkcontrol of mobile robot system via anti-control of chaos,Mechatronic Systems and Control, 48(4), 2020, 239–248.
  17. [17] T.T. Doan and A. Olshevsky, Distributed resource allocationon dynamic networks in quadratic time, Systems & ControlLetters, 99, 2017, 57–63.
  18. [18] X. Jiang and T. Cheng, Design of a BP neural network PIDcontroller for an air suspension system by considering thestiffness of rubber bellows, Alexandria Engineering Journal,74, 2013, 65–78.
  19. [19] R. Zhang and L. Gao, The brushless DC motor control systembased on neural network fuzzy PID control of power electronicstechnology, Optik, 271, 2011, 169879.
  20. [20] S.R. Patil and S.D. Agashe, Auto tuned PID and neural networkpredictive controller for a flow loop pilot plant, MaterialsToday: Proceedings, 72, 2022, 754–760.
  21. [21] Q. Wang and X. Wang, A fault detection diagnosis predictobserver based on resource allocation network, MechatronicSystems and Control, 50(2), 2022, 96–101.
  22. [22] M.S. AbouOmar, Y. Su, H. Zhang, B. Shi, and L. Wan,Observer-based interval type-2 fuzzy PID controller forPEMFC air feeding system using novel hybrid neuralnetwork algorithm-differential evolution optimizer, AlexandriaEngineering Journal, 61, 2022, 7353–7375.
  23. [23] B. Shan, Y. Pang, Q. Zheng, Q. Xu, Y. Wang, Z. Zhu,and F. Zhang, Improved ANFIS combined with PID forextractive distillation process control of benzene–isopropanol–water mixtures, Chemical Engineering Science, 269, 2023, 1–13.
  24. [24] J. Liu and L. Wang, Two-stage vibration-suppressionframework for optimal robust placements design and reliablePID gains design via set-crossing theory and artificial neuralnetwork, Reliability Engineering & System Safety, 230, 2022,108956.63

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