讲席教授 机械与能源工程系   课题组网站

欧洲科学院院士(Academia Europaea, The Academy of Europe)

IEEE Fellow, INNS Fellow, IAPR Fellow

"科睿唯安"高被引学者

IEEE计算智能学会神经网络先驱奖

国际神经网络学会Dennis Gabor终身贡献奖

《Artificial Intelligence Review》主编(Editor-in-Chief)

《IEEE/CAA Journal of Automatica Sinica》副主编(Deputy Editor-in-Chief)

《CAAI Artificial Intelligence Research》副主编(Deputy Editor-in-Chief)

World Scientific出版社Deep Learning Neural Networks系列丛书主编

IEEE广州分会主席

中国自动化学会会士、常务理事

个人简介

刘德荣,南方科技大学讲席教授、博士生导师。1994年从美国圣母大学毕业并获电气工程博士学位。从1999年开始,在芝加哥伊利诺依大学电气与计算机工程系工作,先后任该校助教授(1999–2002)、终身职副教授(2002–2006)和终身职正教授(2006年起)。2008年,入选中国科学院“百人计划”。曾任中国科学院自动化研究所复杂系统管理与控制国家重点实验室副主任(2010–2016)。自1992年以来,共发表了270多篇SCI论文、280多篇国际会议论文。同他人合作共出版过13本书。获得2018年国际神经网络学会终身贡献奖(Dennis Gabor Award for lifetime contributions to engineering applications of neural networks)和2022年IEEE计算智能学会神经网络先驱奖。2017年起连续多年获得Clarivate高被引学者称号。曾任《IEEE神经网络与学习系统汇刊》主编、IFAC理事、亚太神经网络学会主席。现任中国自动化学会常务理事、《人工智能评论》主编。2005年当选IEEE Fellow、2013年当选INNS Fellow、2016年当选IAPR Fellow、2021年当选欧洲科学院院士。

教育背景
1990–1993年,美国圣母大学电气工程系,获博士学位
1984–1987年,中国科学院自动化研究所,获工学硕士学位
1978–1982年,华东工学院(现南京理工大学)机械电子工程系,获工学学士学位

工作经历     
2022–今,南方科技大学讲席教授、博士生导师
2017–2022年,广东工业大学自动化学院特聘教授、博士生导师      
(2015–2016年,北京科技大学自动化学院副院长、教授、博士生导师、教育部钢铁流程先进控制重点实验室主任)
2010–2016年,中国科学院自动化研究所研究员、博士生导师、复杂系统管理与控制国家重点实验室副主任
2008–2009年,中国科学院自动化研究所研究员、博士生导师
1999–今,美国伊利诺伊大学芝加哥分校电气与计算机工程系助教授、终身职副教授、2006年起任终身职正教授
1995–1999年,美国斯蒂文斯理工学院电气与计算机工程系助教授
1993–1995年,美国通用汽车公司研发中心Staff Fellow
1987–1990年,中国科学院研究生院无线电电子学部助教
1982–1984年,北方工业公司国营向阳仪表厂技术员

获奖情况
欧洲科学院院士 (Academia Europaea, The Academy of Europe, https://ae-info.org), 2021
Fellow,电气与电子工程学会,2005
Fellow,国际神经网络学会,2013
Fellow,国际模式识别学会,2016
中国自动化学会会士,2010
IEEE计算智能学会神经网络先驱奖,2022
国际神经网络学会Dennis Gabor终身贡献奖,2018
中国发明协会发明创业奖创新奖一等奖,2021
中国自动化学会自然科学奖一等奖,2017
"科睿唯安"高被引学者, 2017–今
亚太神经网络联合会杰出成就奖,2014
IEEE Systems, Man, and Cybernetics Society Andrew P. Sage最佳汇刊论文奖,2018
IEEE Transactions on Neural Networks and Learning Systems杰出论文奖,2018
IEEE/CAA Journal of Automatica Sinica钱学森论文奖,2018
两次当选IEEE计算智能学会Distinguished Lecturer,2016–2018和2012–2014
国家自然科学基金委“海外杰出青年合作研究基金” (杰青B类),2008  
伊利诺伊大学University Scholar奖,2006
美国国家科学基金会教授早期事业发展奖,1999  
斯蒂文斯理工学院Harvey N. Davis杰出教学奖,1997

学术兼职
2014–今,Artificial Intelligence Review,主编
2014–今,IEEE/CAA Journal of Automatica Sinica,副主编
2021–今,CAAI Artificial Intelligence Research,副主编
2019–今,World Scientific出版社Deep Learning Neural Networks系列丛书主编
2022–2024年、2015–2017年和2006–2008年,三次当选为IEEE计算智能学会理事
2019–今,IEEE广州分会主席  
2018年,亚太神经网络学会主席
2014–2017年,IFAC理事会成员
2013–2020年,《自动化学报》副主编
2011–今,中国自动化学会常务理事
2010–2015年,IEEE Transactions on Neural Networks and Learning Systems,主编
2010–2012年,国际神经网络学会理事
2008年起,在33个国际会议上做过大会报告和邀请报告
24th International Conference on Neural Information Processing (ICONIP 2017),总主席
12th World Congress on Intelligent Control and Automation (WCICA 2016),总主席
2014 IEEE World Congress on Computational Intelligence (WCCI 2014),总主席
International Joint Conference on Neural Networks (IJCNN 2008),程序主席

研究领域

智能控制理论及应用

自适应动态规划与强化学习

复杂工业系统建模与控制

计算智能

智能信息处理


教学

暂无


学术成果 查看更多

简介

自1992年起刘德荣共出版了13本书(包括1本教材、7本专著、5本编著)和6卷Springer LNCS/LNAI文集、270多篇SCI期刊论文、280多篇国际会议论文。目前在Clarivate Web of Science数据库里面总引用15321次,H-index为72。在Google Scholar里面总引用22550次,H-index为83。刘德荣自1992年以来的研究成果总结如下。(1) 早期研究饱和非线性系统,从事非线性系统稳定性方面的研究工作,其主要成果在文献里被命名为“Liu-Michel判据”(Liu-Michels Criterion),解决了饱和非线性作用下系统的稳定性问题,给出了正定矩阵用来构造Lyapunov函数的充要条件,并于1994年出版相关专著一本。(2) 在神经网络方面,开创了递归神经网络的稀疏结构研究工作并成功将成果应用于联想记忆和细胞神经网络,解决了递归神经网络的稀疏设计问题,并提出细胞神经网络的综合设计方法,以及递归神经网络的鲁棒分析方法和设计算法,并于2002年出版相关专著一本。该方向相关工作获2022年IEEE神经网络领域最高奖–IEEE计算智能学会神经网络先驱奖(Neural Network Pioneer Award)。1999年起,跟美国通用汽车公司合作,完成了神经网络和自适应动态规划方法在汽车发动机控制方面的实际应用工作,该项工作获2018年国际神经网络学会终身贡献奖Dennis Gabor Award (Lifetime contribution award for engineering applications of neural networks)。(3) 在最近15年,主要从事自适应动态规划理论和应用研究工作,在该领域出版了三本书(2013年两本、2017年一本),180多篇SCI论文,是国际上该研究领域的领军学者之一。该领域在世界各国学术界都受到高度重视,刘德荣得到了美国国家基金会五个基金项目(包括一个NSF CAREER Award)、其团队得到了中国国家基金委一个杰青B类项目、两个重点项目、两个“优青”项目和八个面上项目以及工业界的支持。自适应动态规划方法跟强化学习是同类方法,是智能控制、优化、信息处理、人工智能和机器学习领域的热点研究方向,前几年Google旗下人工智能围棋(AlphaGo)采用的就是强化学习跟深度学习相结合的算法。

出版著作    

  1. D. Liu and A. N. Michel, Dynamical Systems with Saturation Nonlinearities: Analysis and Design. London: Springer-Verlag, 1994 (ISBN: 0-387-19888-1).
  2. A. N. Michel and D. Liu, Qualitative Analysis and Synthesis of Recurrent Neural Networks. New York: Marcel Dekker, 2002 (ISBN: 0-8247-0767-2. 此书中文版见张化光、季策、王占山译,科学出版社,2004年).
  3. D. Liu and P. J. Antsaklis, Editors, Stability and Control of Dynamical Systems with Applications. Boston, MA: Birkhauser, 2003 (ISBN: 0-8176-3233-6).
  4. H. Zhang and D. Liu, Fuzzy Modeling and Fuzzy Control. Boston, MA: Birkhauser, 2006 (ISBN: 0-8176-4491-1).
  5. F.-Y. Wang and D. Liu, Editors, Advances in Computational Intelligence: Theory and Applications. Singapore: World Scientific, 2006 (ISBN: 981-256-734-8).
  6. A. N. Michel, L. Hou, and D. Liu, Stability of Dynamical Systems: Continuous, Discontinuous and Discrete Systems. Boston, MA: Birkhauser, 2008 (ISBN: 978-0-8176-4486-4. 此书第二版于2015年出版,副标题“On the Role of Monotonic and Non-Monotonic Lyapunov Functions”, ISBN: 978-3-319-15274-5).
  7. F.-Y. Wang and D. Liu, Editors, Networked Control Systems: Theory and Applications. London: Springer, 2008 (ISBN: 978-1-84800-214-2).
  8. H. Zhang, D. Liu, and Z. Wang, Controlling Chaos: Suppression, Synchronization and Chaotification. London: Springer-Verlag, 2009 (ISBN: 978-1-84882-522-2).
  9. F. L. Lewis and D. Liu, Editors, Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Hoboken, NJ: Wiley, 2013 (ISBN: 978-1-118-10420-0).
  10. H. Zhang, D. Liu, Y. Luo, and D. Wang, Adaptive Dynamic Programming for Control: Algorithms and Stability. London: Springer-Verlag, 2013 (ISBN: 978-1-4471-4757-2).
  11. D. Liu, C. Alippi, D. Zhao, and H. Zhang, Editors, Frontiers of Intelligent Control and Information Processing. Singapore: World Scientific, 2014 (ISBN: 978-981-4616-87-4).
  12. J. Keller, D. Liu, and D. Fogel, Fundamentals of Computational Intelligence–Neural Networks, Fuzzy Systems, and Evolutionary Computation. New York: IEEE/Wiley, 2016 (三个计算智能领域IEEE前任主编合写的教材,ISBN: 978-1-119-21434-2).
  13. D. Liu, Q. Wei, D. Wang, X. Yang, and H. Li, Adaptive Dynamic Programming with Applications in Optimal Control. Cham, Switzerland: Springer, 2017 (ISBN: 978-3-319-50813-9).

部分期刊论文

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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

发明专利 

  1. ZL 2012 1 0310212.0,变换炉的控制方法,发明人:刘德荣,魏庆来,黄玉柱,赵冬斌
  2. ZL 2013 1 0728066.8, 一种变换炉系统的炉温自学习控制方法,发明人:刘德荣,魏庆来,李超,徐延才
  3. ZL 2013 1 0516852.1,一种煤气化炉系统的炉温自学习控制方法,发明人:刘德荣,魏庆来,徐延才
  4. ZL 2013 1 0695793.9,一种智能微电网双电池电能协同优化方法,发明人:刘德荣,魏庆来,徐延才
  5. ZL 2014 1 0271465.0,一种带有储能设备的智能微电网电能优化控制方法,发明人:刘德荣,魏庆来,石光
  6. ZL 2014 1 0440567.0,一种智能微电网分布式储能设备控制优化方法,发明人:刘德荣,魏庆来,石光
  7. ZL 2014 1 0828012.3,一种基于神经网络的办公建筑房间分类方法,发明人:刘德荣,石光,魏庆来,刘禹,关强
  8. ZL 2015 1 0504486.7,办公建筑能耗管理方法,发明人:刘德荣,石光,魏庆来
  9. ZL 2020 1 0240156.2,基于可变误差的非线性系统自适应最优控制方法,发明人:刘德荣,魏庆来,林汉权,李超
  10. ZL 2012 1 0291386.7,煤气化炉的控制方法,发明人:赵冬斌,王滨,刘德荣,魏庆来,朱圆恒,苏永生
  11. ZL 2013 1 0036976.X,基于数据的Q函数自适应动态规划方法,发明人: 赵冬斌,朱圆恒,刘德荣
  12. ZL 2013 1 0232043.8,基于监督式强化学习的最优控制方法,发明人: 赵冬斌,王滨,刘德荣
  13. ZL 2013 1 0739109.2,一种基于稀疏强化学习的传感器网络优化方法,发明人: 赵冬斌,张震,刘德荣
  14. ZL 2016 1 0221709.3,基于Q学习的智能楼宇温度控制方法,发明人:魏庆来,李本凯,刘德荣

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南方科技大学刘德荣课题组招聘博士后和研究学者

【导师简介】

刘德荣教授,欧洲科学院院士、IEEE Fellow、INNS Fellow、IAPR Fellow、CAA Fellow、国家级高层次人才计划入选者。曾任《IEEE Transactions on Neural Networks and Learning Systems》主编、亚太神经网络学会主席,现任《Artificial Intelligence Review》主编、IEEE广州分会主席、中国自动化学会常务理事。曾获IEEE计算智能学会神经网络先驱奖、国际神经网络学会Dennis Gabor终身贡献奖和亚太神经网络联合会杰出成就奖。连续多年获"科睿唯安"高被引学者称号,曾在30多个国际会议上做过大会报告和邀请报告,并担任过多个国际大会的总主席和程序主席。共出版了13本书(包括1本教材、7本专著、5本编著)和6卷Springer LNCS/LNAI文集、270多篇SCI期刊论文、280多篇国际会议论文。目前在Clarivate Web of Science数据库里面总引用15321次,H-index为72。在Google Scholar里面总引用22550次,H-index为83。

【招聘方向】

1、自适应动态规划与强化学习

2、智能控制、优化与信息处理
3、深度学习及其在控制领域的应用

【博士后招聘要求】

1、具有自动控制或相关专业的博士学位,有良好的数学基础,在国际一流刊物发表过高水平科研论文两篇以上;

2、为人正直、诚实,品行端正,德才兼备;身心健康,通情达理,为人友善;勤奋踏实,积极上进,具有高度的责任心和良好的团队合作精神;

3、具有良好的专业英语写作和报告能力,具有较强的独立科研能力;

4、年龄不超过35岁。

【博士后待遇】

1、博士后聘用期两年,年薪33万元起,含广东省生活补助15万元及深圳市生活补助6万元,并按深圳市有关规定参加社会保险及住房公积金。博士后福利费参照南方科技大学教职工标准发放。

2、特别优秀候选人可以申请南方科技大学校长卓越博士后,年薪可达50万元以上(含广东省及深圳市补助)。

3、在站期间,可依托学校申请深圳市公租房,未依托学校使用深圳市公租房的博士后,可享受两年税前2800元/月的住房补贴。

4、博士后在站期间拥有优良的工作环境和境内外合作交流机会。

5、博士后出站选择留深从事科研工作,且与本市企事业单位签订3年以上劳动(聘用)合同的,可以申请深圳市博士后留深来深科研资助。深圳市政府给予每人每年10万元科研资助,共资助3年(以深圳市最新申报要求为准)。

【应聘申请材料】

1、 详细的个人简历(附2名推荐人姓名及联系方式),含学习、工作和科研的经历(时间不间断,附近照);

2、 主要科研成果(如论文论著、成果证书或奖励)清单、代表性论文3-5篇以及联系方式;

3、 其他可以证明个人水平和能力的材料。

【应聘方式】

有意者请将申请材料发送至:xiangyj@mail.sustech.edu.cn,邮件标题注明:应聘岗位+本人姓名+毕业学校。

【申请截至日期】

本招聘长期有效,招满为止。
【博士后还是研究学者?】
刚毕业的博士研究生可以申请博士后,毕业两年以上的可以申请研究学者,研究学者待遇一人一议。
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联系地址

518055 广东省深圳市南山区学苑大道1088号南方科技大学

办公电话

0755-

电子邮箱

liudr@sustech.edu.cn

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