Papers

Selected Journal Papers

In Press

• C. Xu, Q. Liu, A resilient distributed optimization algorithm based on consensus of multi-agent system against two attack scenarios, Journal of the Franklin Institute, in press.

Published

• Y. Zheng, Q. Liu, A review of distributed optimization: problems, models and algorithms, Neurocomputing, vol. 483, pp. 446-459, 2022.

• C. Xu, Q. Liu, An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization, Neural Networks, vol. 146, pp. 98-106, 2022.

• H. Liu, J. Zhang, Q. Liu, J. Cao, Minimum spanning tree based graph neural network for emotion classification using EEG, Neural Networks, vol. 145, pp. 308-318, 2022.

• Q. Liu, X. Le, K. Li, A distributed optimization algorithm based on multiagent network for economic dispatch with region partitioning, IEEE Transactions on Cybernetics, vol. 51, no. 5, pp. 2466-2475, May 2021.

• Q. Liu, M. Wang, A projection-based algorithm for optimal formation and optimal matching of multi-robot system, Nonlinear Dynamics, vol. 104, pp. 439-450, 2021.

• D. Yue, J. Cao, Q. Li, Q. Liu, Neural-network-based fully distributed adaptive consensus for a class of uncertain multiagent systems, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2965-2977, July 2021.

• S. Xu, J. Cao, Q. Liu, L. Rutkowski, Optimal control on finite-time consensus of the leader-following stochastic multiagent system with Heuristic method, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3617-3628, June 2021.

• M. Wang, Q. Liu, Y. Zheng, A curvature-segmentation-based minimum time algorithm for autonomous vehicle velocity planning, Information Sciences, vol. 565, pp. 248-261, 2021.

• K. Li, Q. Liu, S. Yang, J. Cao, G. Lu, Cooperative optimization of dual multiagent system for optimal resource allocation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 11, pp. 4676-4687, Nov. 2020.

• X. Xu, Q. Liu, C. Zhang, Z. Zeng, Prescribed performance controller design for DC converter system with constant power loads in DC microgrid, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 11, pp. 4339-4348, Nov. 2020.

• K. Li, Q. Liu, Z. Zeng, Distributed optimisation based on multi-agent system for resource allocation with communication time-delay, IET Control Theory & Applications, vol. 14, no. 4, pp. 549-557, Mar. 2020.

• S. Yang, J. Wang, Q. Liu, Consensus of heterogeneous nonlinear multiagent systems with duplex control laws, IEEE Transactions on Automatic Control, vol. 64, no. 12, pp. 5140-5147, Dec. 2019.

• S. Yang, J. Wang, Q. Liu, Cooperative-competitive multiagent systems for distributed minimax optimization subject to bounded constraints, IEEE Transactions on Automatic Control, vol. 64, no. 4, pp. 1358-1372, Apr. 2019.

• B. Xu, Q. Liu, T. Huang, A discrete-time projection neural network for sparse signal reconstruction with application to face recognition, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 151-162, Jan. 2019.

• X. Xu, C. Zhang, Q. Liu, J. Cao, A. Alsaedi, Adaptive stabilization for a class of uncertain p-normal nonlinear systems via a generalized homogeneous domination technique, Nonlinear Dynamics, vol. 93, no. 2, pp. 847-862, July 2018.

• S. Yang, Q. Liu, J. Wang, A collaborative neurodynamic approach to multiple-objective distributed optimization, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 981-992, Apr. 2018.

• B. Xu, Q. Liu, Iterative projection based sparse reconstruction for face recognition, Neurocomputing, vol. 284, pp. 99-106, 2018.

• Q. Liu, S. Yang, 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.

• S. Yang, Q. Liu, 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.

• Q. Liu, S. Yang, 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.

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

• Q. Liu, J. Wang, \(L_1\)-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.

• Q. Liu, 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.

• Q. Liu, 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.

• Q. Liu, T. Huang, 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.

• Q. Liu, 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.

• Q. Liu, C. Dang, 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.

• Q. Liu, Z. Guo, 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.

• Z. Guo, Q. Liu, 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.

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

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

• Q. Liu, C. Dang, 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.

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

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

• Q. Liu, 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.
  Outstanding Paper Award, IEEE Transactions on Neural Networks, 2011.

• Q. Liu, 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.

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

• Q. Liu, J. Cao, 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.

Selected Conference Papers

• Q. Liu, J. Xiong, S. Yang, Mixed-norm projection-based iterative algorithm for face recognition, in Proc. 16th Int. Symposium on Neural Networks (ISNN 2019), Moscow, Russia, July 10-12 2019, pp. 331-340.

• Q. Liu, W. Zhang, J. Xiong, B. Xu, L. Cheng, A projection-based algorithm for constrained l1-minimization optimization with application to sparse signal reconstruction, in Proc. 8th International Conference on Information Science and Technology (ICIST 2018), Andalusia, Spain, June 30-July 6 2018, pp. 437-443.

• W. Zhang, Q. Liu, Using the center loss function to improve deep learning performance for EEG signal classification, in Proc. 10th International Conference on Advanced Computational Intelligence (ICACI 2018), Xiamen, China, March 29-31, 2018, pp. 578-582.

• B. Xu, Q. Liu, Elastic net based weighted iterative method for image classification, in Proc. 24th International Conference on Neural Information Processing (ICONIP 2017), Guangzhou, China, November 14-18 2017, pp. 481-489.

• J. Shang, Q. Liu, Cognitive load recognition using multi-threshold united complex network, in Proc. 24th International Conference on Neural Information Processing (ICONIP 2017), Guangzhou, China, November 14-18 2017, pp. 490-498.

• Y. Liu, Q. Liu, Convolutional neural networks with large-margin softmax loss function for cognitive load recognition, in Proc. 36th Chinese Control Conference (CCC 2017), Dalian, China, July 26-28 2017, pp. 4045-4049.

• J. Shang, W. Zhang, J. Xiong, Q. Liu, Cognitive load recognition using multi-channel complex network method, in Proc. 14th Int. Symposium on Neural Networks (ISNN 2017), Sapporo, Japan, June 21-26 2017, pp. 466-474.

• Q. Liu, B. Xu, J. Xiong, W. Zhang, Projection particle swarm optimizer, in Proc. 7th International Conference on Information Science and Technology (ICIST 2017), Da Nang, Vietnam, April 16-19 2017, pp. 161-168.

• S. Yang, J. Wang, Q. Liu, Multiple-objective optimization based on a twotime-scale neurodynamic system, in Proc. 8th International Conference on Advanced Computational Intelligence (ICACI 2016), Chiang Mai, Thailand, February 14-16 2016, pp. 193-199.

• W. Zhang, Q. Liu, A one-layer discrete-time projection neural network for support vector classification, in Proc. IEEE International Joint Conference on Neural Networks (IJCNN 2014), Beijing, China, July 6-11 2014, pp. 3143-3148.

• Q. Liu, W. Yang, A discrete-time recurrent neural network with global exponential stability for constrained linear variational inequalities, in Proc. 31st Chinese Control Conference (CCC 2012), Hefei, China, July 25-27 2012, pp. 3296-3301.

• Q. Liu, J. Wang, A one-layer dual neural network with a unipolar hard-limiting activation function for shortest-path routing, in Proc. Artificial Neural Networks - ICANN 2010. Part II, Springer LNCS 6353, 2010, pp. 498-505.

• Q. Liu, J. Wang, A one-layer recurrent neural network for convex programming, in Proc. IEEE International Joint Conference on Neural Networks (IJCNN 2008), Hong Kong, June 1-6 2008, pp. 83-90.

• Q. Liu, J. Wang, A one-layer recurrent neural network with a unipolar hardlimiting activation function for k-winners-take-all operation, in Proc. IEEE International Joint Conference on Neural Networks (IJCNN 2007), Orlando, Florida, USA, Aug. 12-17 2007, pp. 84-89.

• Q. Liu, J. Wang, A recurrent neural network for non-smooth convex programming subject to linear equality and bound constraints, in Proc. 13th Int. Conference on Neural Information Processing (ICONIP 2006), vol. LNCS 4233. Springer, 2006, pp. 1004-1013.

• Q. Liu, J. Wang, J. Cao, A delayed Lagrangian network for solving quadratic programming problems with equality constraints, in Proc. 3rd Int. Symposium on Neural Networks (ISNN 2006), vol. LNCS 3971. Springer, 2006, pp. 369-378.