2018-04-16 15:02   审核人:   (点击: )



















加拿大西蒙弗雷泽大学Ljiljana Trajkovic教授

报告题目:Machine Learning for Complex Networks


台湾国立中兴大学蔡清池(Ching-Chih Tsai)教授

报告题目:Intelligent Adaptive Learning Control Methods with Applications to Mobile Robots and Multirobots




南方科技大学Hisao Ishibuchi教授

报告题目:Fair Performance Comparison of Evolutionary Multi-Objective and Many-Objective Optimization Algorithms





台湾国立科技大学苏顺丰(Shun-Feng Su)教授

报告题目:Decomposed Fuzzy Systems


台湾中山大学黄国胜(Kao-Shing Hwang)教授




    於志文教授    计算机学院党委书记、国家杰青、“万人计划”领军人才


    史豪斌副教授  计算机学院信息安全与电子商务技术系 系主任


    潘炜副教授    计算机学院

    王柱副教授    计算机学院


Ljiljana Trajkovic

School of Engineering Science

Simon Fraser University, Canada

IEEE Follow/Senior Past President (2018–2019) of the IEEE Systems, Man, and Cybernetics Society

Talk Title: Machine Learning for Complex Networks


Collection and analysis of data from deployed networks is essential for understanding modern networks. Traffic traces collected from various deployed communication networks and the Internet have been used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic. Spectral graph theory has been applied to analyze various topologies of complex networks and capture historical trends in their development. Recent machine learning techniques have proved valuable for predicting anomalous traffic behavior and for classifying anomalies in complex networks. Further applications of these tools will help improve our understanding of the underlying mechanisms that govern the behavior of complex networks such as the Internet, social networks (Facebook, LinkedIn, Twitter, Internet blogs, forums, and websites), power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms. They will also help improve performance of these networks and enhance their security.


Ljiljana Trajkovic is currently a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada. She received the Dipl. Ing. degree from University of Pristina, Yugoslavia, in 1974, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, in 1979 and 1981, respectively, and the Ph.D. degree in electrical engineering from University of California at Los Angeles, in 1986.

Dr. Trajkovic serves as IEEE Division Delegate-Elect/Director-Elect (2018). She serves as Senior Past President (2018–2019) of the IEEE Systems, Man, and Cybernetics Society and served as Junior Past President (2016–2017), President (2014–2015), President-Elect (2013), Vice President Publications (2012–2013 and 2010–2011), Vice President Long-Range Planning and Finance (2008–2009), and a Member at Large of its Board of Governors (2004–2006). She is a Professional Member of IEEE-HKN and a Fellow of the IEEE.


蔡清池(Ching-Chih Tsai)

Department of Electrical Engineering

National Chung-Hsing University

IEEE Fellow/IET Fellow /CACS Fellow

Talk Title: Intelligent Adaptive Learning Control Methods with Applications to Mobile Robots and Multirobots


Intelligent adaptive learning control (IALC) has been widely investigated and applied for frontier mobile robotics. By incorporating the merits of predictive control, fuzzy modeling and recurrent fuzzy neural networks, this talk will present you three novel ILC framework or paradigms for a class of mobile robots and multirobots in order to achieve desired motion control and navigation. In the outset of the talk, some literature reviews are first mentioned, and then three novel ILC methods and their applications to wheeled robots and multirobots are briefly highlighted and experimentally demonstrated. First, some stable IALC paradigm using feedforward and recurrent fuzzy neural-network structures are discussed which have been well employed to motion control of uncertain wheeled mobile platforms. Second, some new consensus formation control paradigm using IALC are proposed for a class of mobile multirobots. Third, one IALC paradigm using deep learning is simply presented and shown effective for intelligent mobile robots and multirobots. Last but not least, some perspective topics are recommended for future research.


Ching-Chih Tsai is currently a Distinguished Professor in the Department of Electrical Engineering, National Chung-Hsing University, Taichung, Taiwan, where he served the Chairman in the Department of Electrical Engineering from 2012 to 2014. He received the Diplomat in Electrical Engineering from National Taipei Institute of Technology, Taipei, Taiwan, ROC, the MS degree in Control Engineering from National Chiao Tung University, Hsinchu, Taiwan, ROC and the Ph.D degree in Electrical Engineering from Northwestern University, Evanston, IL, USA, in 1981, 1986 and 1991, respectively.

Dr. Tsai served as the Chair, Taipei Chapter, IEEE Control Systems Society, from 2000 to 2003, and the Chair, Taipei Chapter, IEEE Robotics and Automation Society from 2005 to 2006. He has served as the Chair, Taichung Chapter, IEEE Systems, Man, and Cybernetics Society since 2009, the Chair of IEEE SMC Technical Committee on intelligent learning in control systems since 2009, the President of Robotics Society of Taiwan since 2016, the steering committee of Asian Control Association since 2014, a BOG member of IEEE Nanotechnology council since 2012, the Vice President of International Fuzzy Systems Association since 2015, and a BOG member of the IEEE SMCS since 2017. He is an IEEE Fellow, an IET Fellow and a CACS Fellow.

Hisao Ishibuchi

Chair Professor

Department of Computer Science and Engineering

Southern University of Science Technology

IEEE Fellow

Talk Title: Fair Performance Comparison of Evolutionary Multi-Objective and Many-Objective Optimization Algorithms


Evolutionary multi-objective optimization (EMO) has been an active research area in the field of evolutionary computation. Various EMO algorithms have been proposed in the literature. Their main characteristic feature in comparison with other optimization techniques is that a set of non-dominated solutions (instead of a single optimal solution) is obtained by their single run. This means that the comparison of different EMO algorithms needs an evaluation mechanism of non-dominated solution sets. The focus of this talk is how to compare different EMO algorithms using performance indicators of non-dominated solution sets. In this talk, we first briefly explain evolutionary multi-objective and many-objective optimization algorithms. Next we explain the hypervolume (HV) and the inverted generational distance (IGD), which are the most frequently-used performance indicators. Then we show difficulties of each performance indicator. For example, it is explained that HV-based performance comparison results of different EMO algorithms depends on the specification of a reference point for HV calculation. It is also explained that a set of uniformly distributed non-dominated solutions over the entire Pareto front is not the best distribution of solutions for IGD maximization. Finally, we discuss the parameter specifications for fair performance comparison of different EMO algorithms.


Hisao Ishibuchi is currently with Department of Computer Science and Engineering, SUSTech, Shenzhen, China as a Chair Professor. He received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from Osaka Prefecture University where he has been a professor since 1999.

Dr. Ishibuchi was the IEEE CIS Vice-President for Technical Activities (2010-2013) and an IEEE CIS Distinguished Lecturer (2015-2017). Currently, he is the President of the Japan EC Society (2016-2018), the Editor-in-Chief of IEEE CI Magazine (2014-2019) and Journal of Japan EC Society (2014-2018), an IEEE CIS AdCom member (2014-2019). He is also an Associate Editor of IEEE TEVC (2007-2018), IEEE Access (2013-2018) and IEEE TCyb (2013-2018). He is an IEEE Fellow. In 2018, he was selected in the “Recruitment Program of Global Experts for Foreign Experts” known as the “Thousand Talents Program.”

苏顺丰(Shun-Feng Su)

Department of Electrical Engineering

National Taiwan University of Science and Technology

IEEE Fellow/CACS fellow

Talk Title: Decomposed Fuzzy Systems


In the talk, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables will form the so-called component fuzzy systems. The structure of DFS is proposed to facilitate minimum distribution learning effects among component fuzzy systems so that the learning can be very efficient. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this study to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure. Furthermore, when used in modeling, the proposed DFS not only can have much faster convergent speed, but also can achieve a smaller testing error than those of other fuzzy systems.


Shun-Feng Su is now a Chair Professor of the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, R.O.C. He received the B.S. degree in electrical engineering, in 1983, from National Taiwan University, Taiwan, R.O.C., and the M.S. and Ph.D. degrees in electrical engineering, in 1989 and 1991, respectively, from Purdue University, West Lafayette, IN.

Dr. Su is now the past president of the International Fuzzy Systems Association. He also serves as a board member of various academic societies. He acted as General Chair, Program Chair, or various positions for many international and domestic conferences. Dr. Su currently serves as Associate editors of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, and IEEE Access, a subject editor (Electrical Engineering) of the Journal of the Chinese Institute of Engineers, and the Editor-in-Chief of International Journal of Fuzzy Systems. He is an IEEE Fellow and CACS fellow.

黄国胜(Kao-Shing Hwang)

Department of Electrical Engineering

National Sun Yat-sen University

IET Fellow

Talk Title:以强化学习实现适应性视觉伺服




黄国胜教授现任职于台湾中山大学电机系。其系于1993年获美国西北大学计算机工程博士学位,旋及被台湾中正大学电机系延揽回国任教并继续从事于机器人科学技术方面的研究。这十几年担任过中正大学电算中心组长、代主任、电机系系主任、光机电整合研究所所长、国科会自动控制学门规划及复审委员、自动化学门复审委员。由于其学术表现受认同,因此成为中华民国自动控制学会会士、欧洲电子电机学会(IET)院士、IEEE Trans. on Cybernetics、IEEE/ACM Trans. on Mechatronics编辑、IEEE/CAA Journal of Automatica Sinica编辑,以及International Journal of Fuzzy Systems等编辑也曾受邀为中国上海交通大学荣誉客座教授。黄国胜教授的研究领域与兴趣包含了机器人路径规划、机器足球员系统、强化合作学习系、以及群组机器人任务合作。