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ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

CAAI Transactions on Intelligence Technology 2016年第4期1卷 377-393页

作者： XiaoWang Lingxi Li Yong Yuan Peijun Ye Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Qingduo Academy of Intelligent Industries Qingduo 266100 China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 USA

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

关键词： Social computing Societies 5.0 Parallel intelligence Knowledge automation Cyber-physical-social system Artificial societies Computational ex-periments Parallel execution

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Policy iteration for optimal control of weakly coupled nonlinear systems with completely unknown dynamics

Policy iteration for optimal control of weakly coupled nonli...

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American control Conference

作者： Chao Li Ding Wang Derong Liu Haibo He The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston 02881 USA

ISBN: (纸本)9781467386838

In this paper, an online learning algorithm based on policy iteration is established to solve the optimal control problem for weakly coupled nonlinear continuous-time systems. Using the weak coupling theory, the original problem is transformed into three reduced-order optimal control problems. To obtain the optimal control laws without system dynamics, we construct an online data-based integral policy iteration algorithm which is used to solve the decoupled optimal control problems. The actor-critic technique based on neural networks and the least squares method are used to implement the model-free learning algorithm. A simulation example is given to verify the applicability of the developed algorithm.

关键词： Optimal control Heuristic algorithms Mathematical model System dynamics Nonlinear systems Partitioning algorithms Computational modeling

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Corrigendum to “Road detection algorithm for Autonomous Navigation systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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Recursive Identification of Hammerstein With Noisy Observations

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IFAC-PapersOnLine 2015年第28期48卷 1017-1022页

作者： Mu, Biqiang Han-Fu, Chen Wang, Le Yi Yin, George Key Laboratory of Systems and Control Institute of Systems Science AMSS Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Wayne State University Detroit MI48202 United States Department of Mathematics Wayne State University Detroit MI48202 United States

The paper considers the recursive identification of Hammerstein systems with noisy output, while its input may or may not be corrupted by noise. The system in the latter case is usually called as EIV system. The conditions required in this paper are considerably weaker than those used in previous works, e.g., the orders of the linear subsystem are allowed to be unknown and no additional conditions are imposed on its moving average part. The nonlinearity is general in the sense that a certain class of functions is out of consideration in some previous papers. In the paper, the almost sure convergence together with convergence rate are established for the estimates for coefficients of the linear part, and then the almost sure convergence for the estimates for the nonlinearity at any given points are derived by using kernel functions. The convergence rate for the nonlinearity is also obtained for the case where the system input is available without noise. A numerical example is provided, and the simulation results are consistent with the theoretical analysis. © 2015

关键词： control nonlinearities Nonlinear systems Numerical methods Almost sure convergence Convergence rates Errors in variables Hammerstein system Kernel function Linear subsystems Noisy observations Recursive identification

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Identification of FIR systems with Quantized Inputs and Observations

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IFAC-PapersOnLine 2015年第28期48卷 674-679页

作者： Guo, Jin Wang, Le Yi Yin, George Zhao, Yanlong Zhang, Ji-Feng School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Department of Mathematics Wayne State University DetroitMI48202 United States Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper introduces identification algorithms for finite impulse response systems under quantized output observations and general quantized inputs. While asymptotically efficient algorithms for quantized identification under periodic inputs are available, their counterpart under general inputs has encountered technical difficulties and evaded satisfactory resolutions. Under quantized inputs, this paper resolves this issue with constructive solutions. A two-step algorithm is developed, which demonstrates desired convergence properties including strong convergence, mean-square convergence, convergence rates, asymptotic normality, and asymptotical efficiency in terms of the Cramér-Rao lower bound. Some essential conditions on input excitation are derived that ensure identifiability and convergence. It is shown that by a suitable selection of the algorithm's weighting matrix, the estimates become asymptotically efficient. The strong and mean-square convergence rates are explicitly obtained. Optimal input design is discussed. Numerical examples are included to illustrate the main results of this paper © 2015

关键词： FIR filters Efficiency Identification (control systems) Impulse response Asymptotic efficiency Asymptotically efficient Finite impulse response system Identification algorithms Input design Mean square convergences non periodic input quantization

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Robust Decentralized control of Interconnected Nonlinear systems Subject to Uncertainties and Disturbances via Generalized Extended State Observers 34

Robust Decentralized Control of Interconnected Nonlinear Sys...

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第三十四届中国控制会议

作者： ZHANG Chuanlin YANG ning HE Shuaipeng JIA Ruting Shanghai Engineering Research Center of Intelligent Management and Control for Power Process College of Automation EngineeringShanghai University of Electric Power Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Electrical and Computer Engineering Department California State University

ISBN: (纸本)9789881563897

This paper studies the problem of robust decentralized control for a class of large-scale interconnected systems subject to uncertainties and disturbances via a generalized active disturbance rejection control method. First, a novel extended state observer(ESO) is constructed separately for each subsystem with less dependence of the precise system information and structure. Second, an explicit formula of the decentralized active disturbance rejection control(DADRC) law is presented by utilizing the output feedback domination approach. It is shown that by a delicate analysis procedure, the closed-loop system can be rendered semi-globally asymptotically stable only using decentralized output feedback anti-disturbance controller. Numerical simulations show the efficiency of the proposed method.

关键词： output feedback control global stabilization disturbance rejection control extended state observer

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The Curse of Dimensionality and Variable Selection in Nonlinear Non-parametric System Identification

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IFAC-PapersOnLine 2015年第28期48卷 1279-1284页

作者： Bai, Er-Wei Zhao, Wenxiao Zheng, Wei Xing University of Iowa Department of Electrical and Computer Engineering Iowa52242 United States School of Electronics Electrical Engineering and Computer Science Queens University Belfast United Kingdom Key Laboratory of Systems and Control Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Queens University Beijing China School of Computing Engineering and Mathematics University of Western Sydney Chinese Academy of Sciences PenrithNSW2751 Australia

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Some recent results of variable selection in system identification are presented. Finally, a simulation example is given to justify the theoretical results. © 2015

关键词： Parameter estimation Religious buildings Curse of dimensionality High dimensional Non parametric Simulation example Variable selection

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control on a molecular scale: A perspective

Control on a molecular scale: A perspective

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American control Conference

作者： Rolf Findeisen Martha A. Grover Christian Wagner Michael Maiworm Ruslan Temirov F. Stefan Tautz Murti V. Salapaka Srinivasa Salapaka Richard D. Braatz S.O. Reza Moheimani Otto-von-Guericke-Universit?t Magdeburg Laboratory for Systems Theory and Automatic Control Germany School of Chemical & Biomolecular Engineering Georgia Institute of Technology USA Peter Gruenberg Institute (PGI-3) Juelich Research Center Germany Electrical and Computer Engineering Department University of Minnesota USA Massachusetts Institute of Technology USA Department of Mechanical Engineering The University of Texas at Dalla USA

ISBN: (纸本)9781467386838

Imaging and manipulating objects down to the molecular level plays a crucial role in many disciplines. It allows to unravel molecular phenomena, to form materials with new chemical and physical properties, or to build objects on an atomic scale. Science on the nanoscale is inherently interdisciplinary. It requires knowledge and insight into many fields, spanning from modeling, measuring, imaging, actuation to control. Often feedback control facilitates operation with molecular precision, despite the fact that many physical phenomena at the molecular level are still not well understood, and that stochastic and nonlinear effects are inherently present. This work provides an insight into some of the current control related research activities on a molecular scale. This is done considering examples from different fields: control related to scanning probe microscopy such as atomic force microscopy, controlled self assembly on a molecular scale, control aspects of molecular transport, and the use of control for manipulation of single molecules using macroscopic probe tips. The hope is that control related researchers, who are not experts in these fields, become aware of the opportunities present, which could also drive new theoretical developments.

关键词： Computational modeling Mathematical model Force Atomic force microscopy Numerical models Computational modeling Atomic Force Microscopy numerical models Mathematical Model Force Standing Orders Scale formation Insight Transport Process Physical Phenomena Scanning Probe Microscopy Hopefulness Research Activities chemical and physical properties feedback control

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Identifiability and Parameter Estimation of Linear Continuous-time systems under Irregular and Random Sampling

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IFAC-PapersOnLine 2015年第28期48卷 332-337页

作者： Mu, Biqiang Guo, Jin Wang, Le Yi Yin, George Xu, Lijian Zheng, Wei Xing Key Laboratory of Systems and Control Chinese Academy of Sciences Beijing100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Department of Mathematics Wayne State University DetroitMI48202 United States Department of Electrical and Computer Engineering Technology Farmingdale State College State University of New York FarmingdaleNY11735 United States School of Computing Engineering and Mathematics University of Western Sydney PenrithNSW2751 Australia

This paper considers the problem of identifiability and parameter estimation of single-input-single-output, linear, time-invariant, stable, continuous-time systems under irregular and random sampling schemes. Conditions for system identifiability are established under inputs of exponential polynomial types and a tight bound on sampling density. Identification algorithms of Gauss-Newton iterative types are developed to generate convergent estimate sequences. When the sampled output is corrupted by observation noises, input design, sampling times, and convergent algorithms are intertwined. Persistent excitation (PE) conditions for strongly convergent algorithms are derived. Unlike the traditional identification, the PE conditions under irregular and random sampling involve both sampling times and input values. Under the given PE conditions, iterative and recursive algorithms are developed to estimate the original continuous-time system parameters. The corresponding convergence results are obtained. Several simulation examples are displayed to verify the theoretical results. © 2015

关键词： Continuous time systems Iterative methods Lyapunov methods Nonlinear control systems Parameter estimation Sampling convergence and asymptotical normality Estimation algorithm Identifiability Irregular sampling Linear continuous time system Non linear least squares

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