Faculty
LIU Derong
Chair Professor
Academia Europaea, The Academy of Europe
Derong Liu received the PhD degree in electrical engineering from the University of Notre Dame, USA, in 1994. He became a Full Professor of Electrical and Computer Engineering and of Computer Science at the University of Illinois at Chicago in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2016. He has published 13 books. He received the International Neural Network Society’s Gabor Award in 2018 and the IEEE Computational Intelligence Society Neural Network Pioneer Award in 2022. He has been named a highly cited researcher by Clarivate since 2017. He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, a Fellow of the International Association of Pattern Recognition, and a Member of Academia Europaea (The Academy of Europe).
Research Area
◆Adaptive Dynamic Programming and Reinforcement Learning
Research Area
◆Adaptive Dynamic Programming and Reinforcement Learning
◆Intelligent Control and Information Processing
◆Modeling and Control of Complex Industrial Processes
◆Neural Networks and Computational Intelligence
◆Smart Grid
Work Experience
◆ 2022.07 - present: Chair Professor, Southern University of Science and Technology, Shenzhen, China
◆ 2017.01 - 2022.06: Professor, Guangdong University of Technology, Guangzhou, China
◆ 2008.08 - 2016.12: Professor, Institute of Automation, Chinese Academy of Sciences, Beijing, China
◆ 2006.08 - present: Full professor (tenured), University of Illinois at Chicago, Chicago, Illinois, USA
◆ 2002.08 - 2006.08: Associate professor (tenured), University of Illinois at Chicago, Chicago, Illinois, USA
◆ 1999.08 - 2002.08: Assistant professor, University of Illinois at Chicago, Chicago, Illinois, USA
◆1995.08 - 1999.08: Assistant professor, Stevens Institute of Technology, Hoboken, New Jersey, USA
◆ 1993.10 - 1995.08: Staff fellow, General Motors Research and Development Center, Warren, Michigan, USA
◆1987.07 - 1990.08: Instructor, Graduate School of Chinese Academy of Sciences, Beijing, China
◆ 1982.08 - 1984.08: Product design engineer, China North Industries Corporation, Jilin, China
Education
◆ Ph.D. Electrical Engineering, University of Notre Dame, 1994
◆Modeling and Control of Complex Industrial Processes
◆Neural Networks and Computational Intelligence
◆Smart Grid
Work Experience
◆ 2022.07 - present: Chair Professor, Southern University of Science and Technology, Shenzhen, China
◆ 2017.01 - 2022.06: Professor, Guangdong University of Technology, Guangzhou, China
◆ 2008.08 - 2016.12: Professor, Institute of Automation, Chinese Academy of Sciences, Beijing, China
◆ 2006.08 - present: Full professor (tenured), University of Illinois at Chicago, Chicago, Illinois, USA
◆ 2002.08 - 2006.08: Associate professor (tenured), University of Illinois at Chicago, Chicago, Illinois, USA
◆ 1999.08 - 2002.08: Assistant professor, University of Illinois at Chicago, Chicago, Illinois, USA
◆1995.08 - 1999.08: Assistant professor, Stevens Institute of Technology, Hoboken, New Jersey, USA
◆ 1993.10 - 1995.08: Staff fellow, General Motors Research and Development Center, Warren, Michigan, USA
◆1987.07 - 1990.08: Instructor, Graduate School of Chinese Academy of Sciences, Beijing, China
◆ 1982.08 - 1984.08: Product design engineer, China North Industries Corporation, Jilin, China
Education
◆ Ph.D. Electrical Engineering, University of Notre Dame, 1994
◆M.Sc. Automatic Control Theory and Applications, Institute of Automation, Chinese Academy of Sciences, 1987
◆ B.Sc. Mechanical Engineering, East China Institute of Technology, 1982
Professional Recognition
◆ Member, Academia Europaea (The Academy of Europe), 2021
◆ Fellow, Institute of Electrical and Electronics Engineers (IEEE), 2005
◆ Fellow, International Neural Network Society (INNS), 2013
◆ Fellow, International Association for Pattern Recognition (IAPR), 2016
◆ Fellow, Chinese Association of Automation (CAA), 2010
◆ Editor-in-Chief, Artificial Intelligence Review, 2014–present
◆ Editor-in-Chief, IEEE Trans. on Neural Networks and Learning Systems, 2010–2015
◆ Editor, Series on Deep Learning Neural Networks, World Scientific, 2019–present
◆ Neural Networks Pioneer Award, IEEE Computational Intelligence Society, 2022
◆ Dennis Gabor Award, International Neural Network Society, 2018
◆ Outstanding Achievement Award, Asia Pacific Neural Network Assembly, 2014
◆ Highly Cited Researcher, Clarivate, 2017–present
◆ Chair, IEEE Guangzhou Section, 2019–present
◆ IEEE SMC Society Andrew P. Sage Best Transactions Paper Award, 2018
◆ IEEE Trans. Neural Networks and Learning Systems Outstanding Paper Award, 2018
◆ IEEE/CCA Journal of Automatica Sinica Hsue-Shen Tsien Paper Award, 2018
◆ President, Asia Pacific Neural Network Society, 2018
◆ Member of the Council, International Federation of Automatic Control, 2014–2017
◆ Distinguished Lecturer, IEEE Computational Intelligence Society, 2012–2014 and 2016–2018
◆ Board of Governors, International Neural Network Society, 2010–2012 (elected)
◆ AdCom Member, IEEE Computational Intelligence Society, 2006–2008 (elected), 2015–2017 (elected), and 2022–2024 (elected)
◆ University Scholar, University of Illinois, 2006–2009
◆ CAREER Award, National Science Foundation, 1999
Representative Books
◆ B.Sc. Mechanical Engineering, East China Institute of Technology, 1982
Professional Recognition
◆ Member, Academia Europaea (The Academy of Europe), 2021
◆ Fellow, Institute of Electrical and Electronics Engineers (IEEE), 2005
◆ Fellow, International Neural Network Society (INNS), 2013
◆ Fellow, International Association for Pattern Recognition (IAPR), 2016
◆ Fellow, Chinese Association of Automation (CAA), 2010
◆ Editor-in-Chief, Artificial Intelligence Review, 2014–present
◆ Editor-in-Chief, IEEE Trans. on Neural Networks and Learning Systems, 2010–2015
◆ Editor, Series on Deep Learning Neural Networks, World Scientific, 2019–present
◆ Neural Networks Pioneer Award, IEEE Computational Intelligence Society, 2022
◆ Dennis Gabor Award, International Neural Network Society, 2018
◆ Outstanding Achievement Award, Asia Pacific Neural Network Assembly, 2014
◆ Highly Cited Researcher, Clarivate, 2017–present
◆ Chair, IEEE Guangzhou Section, 2019–present
◆ IEEE SMC Society Andrew P. Sage Best Transactions Paper Award, 2018
◆ IEEE Trans. Neural Networks and Learning Systems Outstanding Paper Award, 2018
◆ IEEE/CCA Journal of Automatica Sinica Hsue-Shen Tsien Paper Award, 2018
◆ President, Asia Pacific Neural Network Society, 2018
◆ Member of the Council, International Federation of Automatic Control, 2014–2017
◆ Distinguished Lecturer, IEEE Computational Intelligence Society, 2012–2014 and 2016–2018
◆ Board of Governors, International Neural Network Society, 2010–2012 (elected)
◆ AdCom Member, IEEE Computational Intelligence Society, 2006–2008 (elected), 2015–2017 (elected), and 2022–2024 (elected)
◆ University Scholar, University of Illinois, 2006–2009
◆ CAREER Award, National Science Foundation, 1999
Representative Books
◆ D. Liu and A. N. Michel, “Asymptotic stability of discrete-time systems with saturation nonlinearities with applications to digital filters,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 39, no. 10, pp. 798–807, Oct. 1992.
◆ D. Liu and A. N. Michel, “Asymptotic stability of systems operating on a closed hypercube,” Systems & Control Letters, vol. 19, no. 4, pp. 281–285, Oct. 1992.
◆ D. Liu and A. N. Michel, “Cellular neural networks for associative memories,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 40, no. 2, pp. 119–121, Feb. 1993.
◆ D. Liu and A. N. Michel, “Null controllability of systems with control constraints and state saturation,” Systems & Control Letters, vol. 20, no. 2, pp. 131–139, Feb. 1993.
◆ D. Liu and A. N. Michel, “Stability analysis of state-space realizations for two-dimensional filters with overflow nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 41, no. 2, pp. 127–137, Feb. 1994.
◆ D. Liu and A. N. Michel, “Sparsely interconnected neural networks for associative memories with applications to cellular neural networks,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 41, no. 4, pp. 295–307, Apr. 1994.
◆ D. Liu and A. N. Michel, “Stability analysis of systems with partial state saturation nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 43, no. 3, pp. 230–232, Mar. 1996.
◆ D. Liu and A. N. Michel, “Robustness analysis and design of a class of neural networks with sparse interconnecting structure,” Neurocomputing, vol. 12, no. 1, pp. 59–76, June 1996.
◆ D. Liu and A. N. Michel, “Asymptotic stability of systems operating on a closed hypercube,” Systems & Control Letters, vol. 19, no. 4, pp. 281–285, Oct. 1992.
◆ D. Liu and A. N. Michel, “Cellular neural networks for associative memories,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 40, no. 2, pp. 119–121, Feb. 1993.
◆ D. Liu and A. N. Michel, “Null controllability of systems with control constraints and state saturation,” Systems & Control Letters, vol. 20, no. 2, pp. 131–139, Feb. 1993.
◆ D. Liu and A. N. Michel, “Stability analysis of state-space realizations for two-dimensional filters with overflow nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 41, no. 2, pp. 127–137, Feb. 1994.
◆ D. Liu and A. N. Michel, “Sparsely interconnected neural networks for associative memories with applications to cellular neural networks,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 41, no. 4, pp. 295–307, Apr. 1994.
◆ D. Liu and A. N. Michel, “Stability analysis of systems with partial state saturation nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 43, no. 3, pp. 230–232, Mar. 1996.
◆ D. Liu and A. N. Michel, “Robustness analysis and design of a class of neural networks with sparse interconnecting structure,” Neurocomputing, vol. 12, no. 1, pp. 59–76, June 1996.
◆ D. Liu, “Cloning template design of cellular neural networks for associative memories,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 44, no. 7, pp. 646–650, July 1997.
◆ D. Liu and Z. Lu, “A new synthesis approach for feedback neural networks based on the perceptron training algorithm,” IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1468–1482, Nov. 1997.
◆ D. Liu, “Lyapunov stability of two-dimensional digital filters with overflow nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 45, no. 5, pp. 574–577, May 1998.
◆ D. Liu, E. I. Sara, and W. Sun, “Nested auto-regressive processes for MPEG-encoded video traffic modeling,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 2, pp. 169–183, Feb. 2001.
◆ D. Liu and A. Molchanov, “Criteria for robust absolute stability of time-varying nonlinear continuous-time systems,” Automatica, vol. 38, no. 4, pp. 627–637, Apr. 2002.
◆ D. Liu, M. E. Hohil, and S. H. Smith, “N-bit parity neural networks: New solutions based on linear programming,” Neurocomputing, vol. 48, no. 1–4, pp. 477–488, Oct. 2002.
◆ D. Liu, T.-S. Chang, and Y. Zhang, “A constructive algorithm for feedforward neural networks with incremental training,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 49, no. 12, pp. 1876–1879, Dec. 2002.
◆ D. Liu, S. Hu, and J. Wang, “Global output convergence of a class of continuous-time recurrent neural networks with time-varying thresholds,” IEEE Transactions on Circuits and Systems-II: Express Briefs, vol. 51, no. 4, pp. 161–167, Apr. 2004.
◆ D. Liu, Y. Zhang, and S. Hu, “Call admission policies based on calculated power control setpoints in SIR-based power-controlled DS-CDMA cellular networks,” Wireless Networks, vol. 10, no. 4, pp. 473–483, July 2004.
◆ D. Liu, X. Xiong, Z.-G. Hou, and B. DasGupta, “Identification of motifs with insertions and deletions in protein sequences using self-organizing neural networks,” Neural Networks, vol. 18, no. 5–6, pp. 835–842, June-July 2005.
◆ D. Liu, Y. Zhang, and H. Zhang, “A self-learning call admission control scheme for CDMA cellular networks,” IEEE Transactions on Neural Networks, vol. 16, no. 5, pp. 1219–1228, Sept. 2005.
◆ D. Liu and Y. Cai, “Taguchi method for solving the economic dispatch problem with nonsmooth cost functions,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 2006–2014, Nov. 2005.
◆ D. Liu, Y. Cai, and G. Tu, “Novel packet coding scheme immune to packet collisions for CDMA-based wireless ad hoc networks,” IEE Proceedings–Communications, vol. 153, no. 1, pp. 1–4, Feb. 2006.
◆ D. Liu, X. Xiong, B. DasGupta, and H. Zhang, “Motif discoveries in unaligned molecular sequences using self-organizing neural networks,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 919–928, July 2006.
◆ D. Liu, S. Hu, and H. Zhang, “Simultaneous blind separation of instantaneous mixtures with arbitrary rank,” IEEE Transactions on Circuits and Systems-I: Regular Papers, vol. 53, no. 10, pp. 2287–2298, Oct. 2006.
◆ D. Liu, Z. Pang, and S. R. Lloyd, “A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG,” IEEE Transactions on Neural Networks, vol. 19, no. 2, pp. 308–318, Feb. 2008.
◆ D. Liu, H. Javaherian, O. Kovalenko, and T. Huang, “Adaptive critic learning techniques for engine torque and air-fuel ratio control,” IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, vol. 38, no. 4, pp. 988–993, Aug. 2008.
◆ D. Liu, D. Wang, D. Zhao, Q. Wei, and N. Jin, “Neural-network-based optimal control for a class of unknown discrete-time nonlinear systems using globalized dual heuristic programming,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 3, pp. 628–634, July 2012.
◆ D. Wang, D. Liu, Q. Wei, D. Zhao, and N. Jin, “Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming,” Automatica, vol. 48, no. 8, pp. 1825–1832, Aug. 2012.
◆ D. Liu, D. Wang, and X. Yang, “An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs,” Information Sciences, vol. 220, pp. 331–342, Jan. 2013.
◆ T. Huang and D. Liu, “A self-learning scheme for residential energy system control and management,” Neural Computing and Applications, vol. 22, no. 2, pp. 259–269, Feb. 2013.
◆ D. Liu and Q. Wei, “Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 779–789, Apr. 2013.
◆ D. Liu, H. Li, and D. Wang, “Neural-network-based zero-sum game for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm,” Neurocomputing, vol. 110, pp. 92–100, June 2013.
◆ D. Liu, Y. Huang, D. Wang, and Q. Wei, “Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming,” International Journal of Control, vol. 86, no. 9, pp. 1554–1566, Sept. 2013.
◆ D. Liu, D. Wang, and H. Li, “Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 2, pp. 418–428, Feb. 2014.
◆ D. Liu and Q. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 621–634, Mar. 2014.
◆ D. Liu, H. Li, and D. Wang, “Online synchronous approximate optimal learning algorithm for multiplayer nonzero-sum games with unknown dynamics,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 44, no.8, pp. 1015–1027, Aug. 2014.
◆ Q. Wei and D. Liu, “Data-driven neuro-optimal temperature control of water-gas shift reaction using stable iterative adaptive dynamic programming,” IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6399–6408, Nov. 2014.
◆ Q. Wei and D. Liu, “Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 4, pp. 1020–1036, Oct. 2014.
◆ D. Liu, P. Yan, and Q. Wei, “Data-based analysis of discrete-time linear systems in noisy environment: Controllability and observability,” Information Sciences, vol. 288, pp. 314–329, Dec. 2014.
◆ D. Liu, D. Wang, F. Wang, H. Li, and X. Yang, “Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2834–2847, Dec. 2014.
◆ Q. Wei, D. Liu, and X. Yang, “Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 4, pp. 866–879, Apr. 2015.
◆ D. Liu, H. Li, and D. Wang, “Error bounds for adaptive dynamic programming algorithms for solving undiscounted optimal control problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1323–1334, June 2015.
◆ D. Liu, X. Yang, D. Wang, and Q. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,” IEEE Transactions on Cybernetics, vol.45, no.7, pp.1372–1385, July 2015.
◆ D. Liu, C. Li, H. Li, D. Wang, and H. Ma, “Neural-network-based decentralized control of continuous-time nonlinear interconnected systems with unknown dynamics,” Neurocomputing, vol. 165, pp. 90–98, Oct. 2015.
◆ D. Liu, Q. Wei, and P. Yan, “Generalized policy iteration adaptive dynamic programming for discrete-time nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1577–1591, Dec. 2015.
◆ Q. Wei, D. Liu, and H. Lin, “Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 840–853, Mar. 2016.
◆ D. Liu, Y. Xu, Q. Wei, and X. Liu, “Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 1, pp. 36–46, Jan. 2018.
◆ B. Zhao and D. Liu(*), “Event-triggered decentralized tracking control of modular reconfigurable robots through adaptive dynamic programming,” IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 3054–3064, Apr. 2020.
◆ D. Liu, S. Xue, B. Zhao, B. Luo, and Q. Wei, “Adaptive dynamic programming for control: A survey and recent advances,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 142–160, Jan. 2021.
◆ B. Zhao, F. Luo, H. Lin, and D. Liu(*), “Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems,” Neural Networks, vol. 134, pp. 54–63, Feb. 2021.
◆ B. Luo, Y. Yang, and D. Liu(*), “Policy iteration Q-learning for data-based two-player zero-sum game of linear discrete-time systems,” IEEE Transactions on Cybernetics, vol. 51, no. 7, pp. 3630–3640, July 2021.
◆ S. Xue, B. Luo, D. Liu(*), and Y. Gao, “Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation,” Neural Networks, vol. 152, pp. 212–223, Aug. 2022.
◆ D. Liu and Z. Lu, “A new synthesis approach for feedback neural networks based on the perceptron training algorithm,” IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1468–1482, Nov. 1997.
◆ D. Liu, “Lyapunov stability of two-dimensional digital filters with overflow nonlinearities,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 45, no. 5, pp. 574–577, May 1998.
◆ D. Liu, E. I. Sara, and W. Sun, “Nested auto-regressive processes for MPEG-encoded video traffic modeling,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 2, pp. 169–183, Feb. 2001.
◆ D. Liu and A. Molchanov, “Criteria for robust absolute stability of time-varying nonlinear continuous-time systems,” Automatica, vol. 38, no. 4, pp. 627–637, Apr. 2002.
◆ D. Liu, M. E. Hohil, and S. H. Smith, “N-bit parity neural networks: New solutions based on linear programming,” Neurocomputing, vol. 48, no. 1–4, pp. 477–488, Oct. 2002.
◆ D. Liu, T.-S. Chang, and Y. Zhang, “A constructive algorithm for feedforward neural networks with incremental training,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, vol. 49, no. 12, pp. 1876–1879, Dec. 2002.
◆ D. Liu, S. Hu, and J. Wang, “Global output convergence of a class of continuous-time recurrent neural networks with time-varying thresholds,” IEEE Transactions on Circuits and Systems-II: Express Briefs, vol. 51, no. 4, pp. 161–167, Apr. 2004.
◆ D. Liu, Y. Zhang, and S. Hu, “Call admission policies based on calculated power control setpoints in SIR-based power-controlled DS-CDMA cellular networks,” Wireless Networks, vol. 10, no. 4, pp. 473–483, July 2004.
◆ D. Liu, X. Xiong, Z.-G. Hou, and B. DasGupta, “Identification of motifs with insertions and deletions in protein sequences using self-organizing neural networks,” Neural Networks, vol. 18, no. 5–6, pp. 835–842, June-July 2005.
◆ D. Liu, Y. Zhang, and H. Zhang, “A self-learning call admission control scheme for CDMA cellular networks,” IEEE Transactions on Neural Networks, vol. 16, no. 5, pp. 1219–1228, Sept. 2005.
◆ D. Liu and Y. Cai, “Taguchi method for solving the economic dispatch problem with nonsmooth cost functions,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 2006–2014, Nov. 2005.
◆ D. Liu, Y. Cai, and G. Tu, “Novel packet coding scheme immune to packet collisions for CDMA-based wireless ad hoc networks,” IEE Proceedings–Communications, vol. 153, no. 1, pp. 1–4, Feb. 2006.
◆ D. Liu, X. Xiong, B. DasGupta, and H. Zhang, “Motif discoveries in unaligned molecular sequences using self-organizing neural networks,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 919–928, July 2006.
◆ D. Liu, S. Hu, and H. Zhang, “Simultaneous blind separation of instantaneous mixtures with arbitrary rank,” IEEE Transactions on Circuits and Systems-I: Regular Papers, vol. 53, no. 10, pp. 2287–2298, Oct. 2006.
◆ D. Liu, Z. Pang, and S. R. Lloyd, “A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG,” IEEE Transactions on Neural Networks, vol. 19, no. 2, pp. 308–318, Feb. 2008.
◆ D. Liu, H. Javaherian, O. Kovalenko, and T. Huang, “Adaptive critic learning techniques for engine torque and air-fuel ratio control,” IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, vol. 38, no. 4, pp. 988–993, Aug. 2008.
◆ D. Liu, D. Wang, D. Zhao, Q. Wei, and N. Jin, “Neural-network-based optimal control for a class of unknown discrete-time nonlinear systems using globalized dual heuristic programming,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 3, pp. 628–634, July 2012.
◆ D. Wang, D. Liu, Q. Wei, D. Zhao, and N. Jin, “Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming,” Automatica, vol. 48, no. 8, pp. 1825–1832, Aug. 2012.
◆ D. Liu, D. Wang, and X. Yang, “An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs,” Information Sciences, vol. 220, pp. 331–342, Jan. 2013.
◆ T. Huang and D. Liu, “A self-learning scheme for residential energy system control and management,” Neural Computing and Applications, vol. 22, no. 2, pp. 259–269, Feb. 2013.
◆ D. Liu and Q. Wei, “Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 779–789, Apr. 2013.
◆ D. Liu, H. Li, and D. Wang, “Neural-network-based zero-sum game for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm,” Neurocomputing, vol. 110, pp. 92–100, June 2013.
◆ D. Liu, Y. Huang, D. Wang, and Q. Wei, “Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming,” International Journal of Control, vol. 86, no. 9, pp. 1554–1566, Sept. 2013.
◆ D. Liu, D. Wang, and H. Li, “Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 2, pp. 418–428, Feb. 2014.
◆ D. Liu and Q. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 621–634, Mar. 2014.
◆ D. Liu, H. Li, and D. Wang, “Online synchronous approximate optimal learning algorithm for multiplayer nonzero-sum games with unknown dynamics,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 44, no.8, pp. 1015–1027, Aug. 2014.
◆ Q. Wei and D. Liu, “Data-driven neuro-optimal temperature control of water-gas shift reaction using stable iterative adaptive dynamic programming,” IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6399–6408, Nov. 2014.
◆ Q. Wei and D. Liu, “Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 4, pp. 1020–1036, Oct. 2014.
◆ D. Liu, P. Yan, and Q. Wei, “Data-based analysis of discrete-time linear systems in noisy environment: Controllability and observability,” Information Sciences, vol. 288, pp. 314–329, Dec. 2014.
◆ D. Liu, D. Wang, F. Wang, H. Li, and X. Yang, “Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2834–2847, Dec. 2014.
◆ Q. Wei, D. Liu, and X. Yang, “Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 4, pp. 866–879, Apr. 2015.
◆ D. Liu, H. Li, and D. Wang, “Error bounds for adaptive dynamic programming algorithms for solving undiscounted optimal control problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 6, pp. 1323–1334, June 2015.
◆ D. Liu, X. Yang, D. Wang, and Q. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,” IEEE Transactions on Cybernetics, vol.45, no.7, pp.1372–1385, July 2015.
◆ D. Liu, C. Li, H. Li, D. Wang, and H. Ma, “Neural-network-based decentralized control of continuous-time nonlinear interconnected systems with unknown dynamics,” Neurocomputing, vol. 165, pp. 90–98, Oct. 2015.
◆ D. Liu, Q. Wei, and P. Yan, “Generalized policy iteration adaptive dynamic programming for discrete-time nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1577–1591, Dec. 2015.
◆ Q. Wei, D. Liu, and H. Lin, “Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 840–853, Mar. 2016.
◆ D. Liu, Y. Xu, Q. Wei, and X. Liu, “Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 1, pp. 36–46, Jan. 2018.
◆ B. Zhao and D. Liu(*), “Event-triggered decentralized tracking control of modular reconfigurable robots through adaptive dynamic programming,” IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 3054–3064, Apr. 2020.
◆ D. Liu, S. Xue, B. Zhao, B. Luo, and Q. Wei, “Adaptive dynamic programming for control: A survey and recent advances,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 142–160, Jan. 2021.
◆ B. Zhao, F. Luo, H. Lin, and D. Liu(*), “Particle swarm optimized neural networks based local tracking control scheme of unknown nonlinear interconnected systems,” Neural Networks, vol. 134, pp. 54–63, Feb. 2021.
◆ B. Luo, Y. Yang, and D. Liu(*), “Policy iteration Q-learning for data-based two-player zero-sum game of linear discrete-time systems,” IEEE Transactions on Cybernetics, vol. 51, no. 7, pp. 3630–3640, July 2021.
◆ S. Xue, B. Luo, D. Liu(*), and Y. Gao, “Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation,” Neural Networks, vol. 152, pp. 212–223, Aug. 2022.