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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Deyuan Meng Yingmin Jia Junping Du Fashan Yu the Department of Systems and Control Beihang University (BUAA) the Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and TechnologyBeijing University of Posts and Telecommunications the School of Electrical Engineering and Automation Henan Polytechnic University

This paper is concerned with the iterative learning control(ILC) problem for discrete-time systems with iterationvarying *** the so-called super-vector approach to ILC,the discrete domain bounded real lemma is employed to develop a sufficient condition ensuring both the stability and the desired H∞ performance of the ILC *** is shown that this sufficient condition can be presented in terms of linear matrix inequalities(LMIs),which can also determine the learning gain matrix.A numerical simulation example is included to validate the theoretical results.

关键词： Iterative learning control discrete-time systems iteration-varying disturbances H_∞ performance linear matrix inequality

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Distributed H∞ consensus control for networks of multiple agents with linear coupling dynamics

Distributed H∞ consensus control for networks of multiple a...

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SICE Annual Conference

作者： Yang Liu Yingmin Jia Junping Du Fashan Yu Department of Systems and Control Beihang University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

This paper is devoted to the consensus control for a network of autonomous agents with high-dimension linear coupling dynamics and subject to external disturbances. By transforming the consensus control problem into an H infin control problem, we propose a distributed state feedback protocol, and obtain conditions in terms of linear matrix inequalities (LMIs) to ensure the consensus with a prescribed H infin performance level for networks with zero and nonzero communication delays, respectively. Furthermore, the undetermined feedback matrix of the proposed protocol is also solved. A numerical example is included to validate the theoretical results.

关键词： Control systems Communication system control Protocols Multiagent systems Vehicle dynamics Automatic control Telecommunication control Electronic mail Linear feedback control systems State feedback

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Adaptive multi-class support vector machine for microarray classification and gene selection

Adaptive multi-class support vector machine for microarray c...

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SICE Annual Conference

作者： Juntao Li Yingmin Jia Junping Du Fashan Yu Department of Systems and Control Beihang University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

This paper proposes an adaptive multi-class support vector machine for simultaneous microarray classification and gene selection. By evaluating the gene ranking significance, the adaptive multi-class support vector machine is shown to encourage an adaptive grouping effect in the process of building classifiers, thus leading a sparse multi-classifiers with enhanced interpretability. Based on a reasonable correlation between the two regularization parameters, an efficient solution path algorithm is developed for solving the proposed support vector machine. Experiments performed on the leukaemia data set are provided to verify the obtained results.

关键词： Support vector machines Support vector machine classification Electronic mail Programmable control Adaptive control Gene expression Learning systems Automatic control Control systems Telecommunication control

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Automated Design of Logic Circuits with a Increasable Evolution Approach

Automated Design of Logic Circuits with a Increasable Evolut...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Guoliang He Naixue Xiong Athanasios V. Vasilakos Yuanxiang Li Zhongzhi Shi State Key Laboratory of Software Engineering Wuhan University of China Wuhan China Key Laboratory of Intelligent Information Process Institute of Computing Technology Chinese Academy and Sciences Beijing China Department of Computer Science Georgia State University USA Department of Computer Science Saint Francis Xavier University Canada

Since the scalability of logic circuits is becoming larger and more complex, the auto-design is becoming more and more difficult. In order to improve automatic design and performance evaluation of logic circuits in efficiency and capability of optimization, multiobjective simulated annealing (MSA) based increasable evolution approach is designed to evolve logic circuits automatically with an extended matrix encoding method, which can be able to reflect the potential performance of a circuit and reduce the risk of deleting a circuit with a good developing potential during evolution is devised. In the process of evolution, each individual is renewedly associated to a corresponding objective in terms of a novel adaptive evaluation method at each generation. In experiments, complicated arithmetic circuits are designed to assess the performance of MSA against other algorithms. Results indicate that the proposed method could design logic circuits efficiently.

关键词： Logic circuits Encoding Hardware Scalability Circuit simulation Simulated annealing Genetic algorithms Laboratories Computer science Arithmetic

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;intelligent Techniques for Web Personalization and Recommender Systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Solving Sum and Product Riddle via BDD-based model checking

Solving Sum and Product Riddle via BDD-based model checking

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2008 IEEE/WIC/ACM International Conference on Web Intelligence and intelligent Agent technology - Workshops, WI-IAT Workshops 2008

作者： Luo, Xiangyu Su, Kaile Sattar, Abdul Chen, Yan Tsinghua National Laboratory for Information Science and Technology School of Software Tsinghua University Beijing 100084 China Department of Computer Science Guilin University of Electronic Technology Guilin 541004 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Institute for Integrated and Intelligent Systems Griffith University Brisbane QLD 4111 Australia

ISBN: (纸本)9780769534961

We model the Sum and Product Riddle inpublic announcement logic, which is interpreted on an epistemic Kripke model. The model is symbolically represented as a finite state program with n agents. A model checking method to the riddle is developed by using the BDD-based symbolic model checking algorithm for logic of knowledge we developed in [7]. The method is implemented by extending the model checker MCTK [7] and then the solution of the riddle is verified successfully. © 2008 IEEE.

关键词： Model checking

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Pointwise Logic on Completely Distributive Lattices and Approximate Reasoning

Pointwise Logic on Completely Distributive Lattices and Appr...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yalin Zheng Guang Yang Jing Zheng Jinsheng Xing State Key Laboratory for Intelligent Technology and Systems Department of Automation Faculty of Information Science and Technology Tsinghua University Beijing China Research Center of Intelligent Information Technology Department of Computer Science and Technology Faculty of Software Dongguan University of Science and Technology Dongguan China Research Center of Dongguan Economy Department of Economy and Commerce Dongguan University of Science and Technology Dongguan China Faculty of Mathematics and Computer Science Shanxi Normal University Linfen China Research Center of System Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

In this paper, we propose the basic framework of point- wise topological logic on completely distributive lattices and explore approximate reasoning in it. The logic of this paper is based on pointwise characterization, therefore the pointwise conception is pervasive. We explore approximate reasoning in abstract logical framework Fl on completely distributive lattice L. We propose the structure of point- wise topological logic F TL , the structure of matching function sigma. and the structure of matching neighborhood group. We investigate approximate reasoning in pointwise topological logic F TL with matching function sigma, develop pointwise topological algorithm of simple approximate reasoning, introduce the essential characteristics of this scheme.

关键词： Lattices Fuzzy logic Computer science Knowledge engineering Fuzzy systems Information technology Laboratories intelligent systems Automation Information science

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Solving Sum and Product Riddle via BDD-Based Model Checking

Solving Sum and Product Riddle via BDD-Based Model Checking

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xiangyu Luo Kaile Su Abdul Sattar Yan Chen Tsinghua National Laboratory for Information Science and Technology Key Laboratory of Security for Information System of Ministry of Education School of Software Tsinghua University Beijing China Department of Computer Science Guilin University of Electronic and Technology Guilin China School of Electronics Engineering and Computer Science Peking University Beijing China Institute for Integrated and Intelligent Systems Griffith University Brisbane QLD Australia

We model the sum and product riddle in public announcement logic, which is interpreted on an epistemic Kripke model. The model is symbolically represented as a finite state program with n agents. A model checking method to the riddle is developed by using the BDD-based symbolic model checking algorithm for logic of knowledge we developed in [7]. The method is implemented by extending the model checker MCTK [7] and then the solution of the riddle is verified successfully.

关键词： Data structures Boolean functions IEEE news intelligent agent Educational technology Automatic logic units Laboratories Computer science Information science Computer security

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Research on rough set theory and applications in China

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Lecture Notes in Computer Science

作者： Wang, Guoyin Zhang, Qinghua Huang, Houkuan Ye, Dongyi Hu, Qinghua Hu, Xuegang Shi, Zhongzhi Li, Yongli Shang, Lin An, Liping Sai, Ying Chen, Shanben Liang, Jiye Qin, Keyun Zeng, Huanglin Xie, Keming Miao, Duoqian Min, Fan Wu, Zhaocong Wu, Weizhi Dai, Jianhua Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China College of Math. and Computer Fuzhou University Fuzhou 350002 China Harbin Institute of Technology PO Box 458 Harbin 150006 China School of Computer and Information Hefei University of Technology Hefei 230009 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Lanzhou University No.222 Tianshui South Rd. Lanzhou 730000 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Management Science and Engineering Department Business School Nankai University Tianjin 300071 China Shandong University of Finance No.40 Shun-geng Road Jinan 250014 China Welding Engineering Institute Shanghai Jiaotong University Shanghai 200030 China School of Computer and Information Technology Shanxi University Taiyuan 030006 China Intelligent Control Development Center Southwest Jiaotong University Chengdu 610031 China Artificial Intelligence of Key Laboratory Sichuan University of Science and Engineering Zigong 643000 China Department of Automation College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China 4800 CaoAn Highway Shanghai 201804 China School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China School of Remote Sensing Information Engineering Wuhan University Wuhan 430079 China Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049 China Information College Zhejiang Ocean University Zhejiang 316004 China Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

ISBN: (纸本)3540850635

This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description of the following things: Chinese research groups on rough set with their URLs for web pages, names of principal researchers (supervisors), numbers of graduate students, and topics being investigated. Statistical summaries showing the growth in the research on rough set theory and application in China are included. In addition, an introduction summarizing the research interests of Chinese researchers is included in this article. The contribution of this article is a complete overview of the principal research directions in rough set theory and its applications in China. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Rough set theory

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An improved Bayesian network structure learning algorithm and its application in an intelligent B2C portal

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Web Intelligence and Agent Systems 2007年第2期5卷 127-138页

作者： Ji, Junzhong Liu, Chunnian Yan, Jing Zhong, Ning College of Computer Science and Technology Beijing University of Technology Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing 100022 China Department of Information Engineering Maebashi Institute of Technology 460-1 Kamisadori-Cho Maebashi-City 371-0816 Japan

Web Intelligence (WI) is a new and active research field in current AI and IT. intelligent B2C Portals are an important research topic in WI. In this paper, we first investigate and analyze the architecture of a B2C portal for personalized recommendation from the viewpoint of conceptual levels of WI. Aiming at knowledge-level data mining in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, reducing Conditional Independence (CI) test costs by few lower order CI tests and accelerating search process by means of sort order for candidate parent nodes. Experimental results on benchmark ALARM data sets show that the improved algorithm has high accuracy, and is more efficient in the time performance than other algorithms. Finally, we apply this algorithm to learning Customer Shopping Model (CSM) in an intelligent recommendation system. By a number of experiments on real world data, we find that the recommendation method based on the learned CSM outperforms some traditional ones in rates of coverage and precision. © 2007 - IOS Press and the authors. All rights reserved.

关键词： Bayesian networks

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