头像
Qingshan Liu
Professor
School of Mathematics
Tel:
Email:
qsliu@seu.edu.cn
Address:
Post:
211189
  • Qingshan Liu is currently a professor at the School of Mathematics, Southeast University, Nanjing, China.
    Working Experience
  • 2018/03-now, School of Mathematics, Southeast University, Professor
  • 2014/05-2018/03, School of Automation, Huazhong University of Science and Technology, Professor
  • 2008/09-2014/04, School of Automation, Southeast University, Associate Professor
  • 2016/05-2016/06, Department of Computer Science, City University of Hong Kong, Research Fellow
  • 2013/05-2013/07, Mathematics and Science, Texas A&M University at Qatar, Research Associate
  • 2010/02-2010/08, Mechanical and Automation Engineering, The Chinese University of Hong Kong, Postdoctoral Fellow
  • 2009/08-2009/11, Manufacturing Engineering and Engineering Management, City University of Hong Kong, Senior Research Associate
  • Education Background
  • 2005/08-2008/07, Mechanical and Automation Engineering, The Chinese University of Hong Kong, Ph.D
  • 2002/09-2005/03, Mathematics, Southeast University, M.S.
  • 1997/09-2001/07, Mathematics, Anhui Normal University, B.S.
  • Research Interests

          Neural networks; Computational intelligence; Distributed optimization; Multi-agent systems

    Journal Publications

    1. X. Xu, C. Zhang, Q. Liu, J. Cao, and A. Alsaedi, “Adaptive stabilization for a class of uncertain p-normal nonlinear systems via a generalized homogeneous domination technique,” Nonlinear Dynamics, 2018, in press.

    2. B. Xu and Q. Liu, “Iterative projection based sparse reconstruction for face recognition,” Neurocomputing, vol. 284, pp. 99–106, 2018.

    3. Q. Liu, S. Yang, and Y. Hong, “Constrained consensus algorithms with fixed step size for distributed convex optimization over multiagent networks,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 4259–4265, Aug. 2017.

    4. S. Yang, Q. Liu, and J. Wang, “A collaborative neurodynamic approach to multiple-objective distributed optimization,” IEEE Transactions on Neural Networks and Learning Systems, 2017, in press.

    5. S. Yang, Q. Liu, and J. Wang, “A multi-agent system with a proportional-integral protocol for distributed constrained optimization,” IEEE Transactions on Automatic Control, vol. 62, no. 7, pp. 3461–3467, July 2017.

    6. Q. Liu, S. Yang, and J. Wang, “A collective neurodynamic approach to distributed constrained optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 1747–1758, Aug. 2017.

    7. S. Yang, Q. Liu, and J. Wang, “Distributed optimization based on a multi-agent system in the presence of communication delays,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 5, pp. 717–728, May 2017.

    8. Q. Liu and J. Wang, “L1-minimization algorithms for sparse signal reconstruction based on a projection neural network,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 698–707, Mar. 2016.

    9. Q. Liu and J. Wang, “A second-order multi-agent network for bound-constrained distributed optimization,” IEEE Transactions on Automatic Control, vol. 60, no. 12, pp. 3310–3315, Dec. 2015.

    10. Q. Liu and J. Wang, “A projection neural network for constrained quadratic minimax optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 11, pp. 2891–2900, Nov. 2015.

    11. Q. Liu, T. Huang, and J. Wang, “One-layer continuous- and discrete-time projection neural networks for solving variational inequalities and related optimization problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, pp. 1308–1318, July 2014.

    12. Q. Liu and J. Wang, “A one-layer projection neural network for nonsmooth optimization subject to linear equalities and bound constraints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 5, pp. 812–824, May 2013.

    13. Q. Liu, C. Dang, and T. Huang, “A one-layer recurrent neural network for real-time portfolio optimization with probability criterion,” IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 14–23, Feb. 2013.

    14. Q. Liu and T. Huang, “A neural network with a single recurrent unit for associative memories based on linear optimization,” Neurocomputing, vol. 118, pp. 263–267, Oct. 2013.

    15. Q. Liu, Z. Guo, and J. Wang, “A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization,” Neural Networks, vol. 26, pp. 99–109, Feb. 2012.

    16. Z. Guo, Q. Liu, and J. Wang, “A one-layer recurrent neural network for pseudoconvex optimization subject to linear equality constraints,” IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 1892–1900, Dec. 2011.

    17. Q. Liu and J. Wang, “A one-layer recurrent neural network for constrained nonsmooth optimization,” IEEE Transactions on Systems, Man and Cybernetics-Part B, vol. 41, no. 5, pp. 1323–1333, Oct. 2011.

    18. Q. Liu and J. Cao, “Global exponential stability of discrete-time recurrent neural network for solving quadratic programming problems subject to linear constraints,” Neurocomputing, vol. 74, no. 17, pp. 3494–3501, Oct. 2011.

    19. Q. Liu and J. Wang, “Finite-time convergent recurrent neural network with a hardlimiting activation function for constrained optimization with piecewise-linear objective functions,” IEEE Transactions on Neural Networks, vol. 22, no. 4, pp. 601–613, Apr. 2011.

    20. Q. Liu, C. Dang, and J. Cao, “A novel recurrent neural network with one neuron and finite-time convergence for k-winners-take-all operation,” IEEE Transactions on Neural Networks, vol. 21, no. 7, pp. 1140–1148, July 2010.

    21. Q. Liu, J. Cao, and G. Chen, “A novel recurrent neural network with finite-time convergence for linear programming,” Neural Computation, vol. 22, no. 11, pp. 2962–2978, 2010.

    22. Q. Liu and J. Cao, “A recurrent neural network based on projection operator for extended general variational inequalities,” IEEE Transactions on Systems, Man and Cybernetics-Part B, vol. 40, no. 3, pp. 928–938, June 2010.

    23. Q. Liu and Y. Yang, “Global exponential system of projection neural networks for system of generalized variational inequalities and related nonlinear minimax problems,” Neurocomputing, vol. 73, no. 10–12, pp. 2069–2076, June 2010.

    24. Q. Liu and J. Wang, “A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming,” IEEE Transactions on Neural Networks, vol. 19, no. 4, pp. 558–570, Apr. 2008.

    25. Q. Liu and J. Wang, “A one-layer recurrent neural network with a discontinuous activation function for linear programming,” Neural Computation, vol. 20, no. 5, pp. 1366–1383, 2008.

    26. Q. Liu and J. Wang, “Two k-winners-take-all networks with discontinuous activation functions,” Neural Networks, vol. 21, no. 2–3, pp. 406–413, 2008.

    27. Q. Liu and J. Cao, “Improved global exponential stability criteria of cellular neural networks with time-varying delays,” Mathematical and Computer Modelling, vol. 43, no. 3–4, pp. 423–432, 2006.

    28. Q. Liu, J. Cao, and Y. Xia, “A delayed neural network for solving linear projection equations and its analysis,” IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 834–843, July 2005.

    29. Q. Liu and J. Cao, “Invariant set and attractor of nonautonomous functional differential systems: a decomposition approach,” Nonlinear Dynamics, vol. 37, no. 1, pp. 19–29, 2004.

  • Honors and Awards

    1. 2015, Natural Science Award (Second Class), Hubei Province

    2. 2015, Outstanding Reviewer Award, IEEE Transactions on Cybernetics

    3. 2014-2017, Most Cited Chinese Researcher, Elsevier

    4. 2012, New Century Excellent Talents in University, Ministry of Education of China

    5. 2012, Young Researcher Award, Asia Pacific Neural Network Assembly (APNNA)

    6. 2011, Natural Science Award (First Class), Ministry of Education of China

    7. 2011, Outstanding Paper Award, IEEE Transactions on Neural Networks

    8. 2009, Young Science Star, Science News and Elsevier

    9. 2006, Excellent Master Thesis Award, Jiangsu Province


    Research Grants

    1. National Natural Science Foundation of China (Grant No. 61473333), 'Sparse Representation Algorithms Based on Neural Networks and Swarm Intelligence', 2015/01-2018/12, Lead PI       

    2. New Century Excellent Talents in University of China (Grant No. NCET-12-0114), 'Computational Intelligence', 2013/01-2015/12, Lead PI

    3. National Natural Science Foundation of China (Grant No. 61105060), 'Finite-timeConvergence Based Optimal Design, Analysis and Applications of Recurrent Neural Networks', 2012/01-2014/12, Lead PI

    4. Natural Science Foundation of Jiangsu Province of China (Grant No. BK2011594), 'Optimization Neural Networks Modelling and Finite-time Convergence Analysis', 2012/01-2014/12, Lead PI

    5. Doctoral Program of Ministry of Education (Grant No. 20090092120026), 'Coupling Optimization Neural Network Design Based on Multi-objective Programming Conditions', 2010/01-2012/12, Lead PI

  • Associate Editor

    1. 2018-,IEEE Transactions on Neural Networks and Learning Systems

    2. 2015-,IEEE Transactions on Cybernetics

    3. 2012-,Neural Networks


    Guest Editor

    • Neurocomputing (2015-2016); Cognitive Computation (BICS 2012); Mathematics and Computers in Simulation (ISNN 2010); Computational and Mathematical Methods in Medicine (Computational Neuroscience2013)