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

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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A generalized piecewise quadratic lyapunov function approach to estimating the domain of attraction of a saturated system

A generalized piecewise quadratic lyapunov function approach...

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1st IFAC Conference on Modelling, Identification and control of Nonlinear systems, MICNON 2015

作者： Li, Yuanlong Lin, Zongli Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA229044743 United States

This paper revisits the problem of estimating the domain of attraction of a saturated system with an algebraic loop. A piecewise quadratic Lyapunov function of an augmented state vector composing of the system state and the saturated input has been proposed for use in estimating the domain of attraction for a saturated system. Considering the relationship between the system states and the saturation function, we propose in this paper a generalized piecewise quadratic Lyapunov function, which results from adding a term that characterizes the regional sector condition of the saturated input to the piecewise quadratic Lypunov function. The matrix associated with the generalized piecewise quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability conditions are established, from which a larger estimate of the domain of attraction can be obtained. Simulation results indicate that the effectiveness and superiority of the proposed method.

关键词： Lyapunov functions

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Boundary feedback control of open canals with a Riemann invariants approach

Boundary feedback control of open canals with a Riemann inva...

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作者： Cen, Lihui Xi, Yugeng Li, Dewei Cen, Yigang Department of Control Engineering Central South University Changsha Hunan Province China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Department of Automation Shanghai Jiaotong University Shanghai200240 China School of Computer and Information Technology Beijing Jiaotong University Beijing China

This paper proposes a boundary feedback control design for open canal networks using the linearization of boundary conditions. For open canal networks with any types of cross-sections, which can be modelled by the Saint-Venant equations, the characteristic form in terms of Riemann invariants has been established. Under this established characteristic form, the stabilizing boundary control law has been developed by linearizing the boundary conditions for both a single reach and the open-channel network composed by multi-reaches in a cascade. The design of the boundary feedback control laws for both a single canal and the cascaded networks is illustrated in a unified framework, which extends the results in the literature. © The Author(s) 2014.

关键词： Boundary conditions

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The benefits of conflict avoidance in collaboration

The benefits of conflict avoidance in collaboration

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IEEE International Conference on Cognitive Informatics

作者： Haibin Zhu Yin Sheng Xianzhong Zhou Department of Computer Science and Mathematics Nipissing University North Bay Canada Department of Control and System Engineering Nanjing University Nanjing China

ISBN: (纸本)9781467372916

Conflict management is an important and challenging problem in collaboration. Conflict avoidance is one way to manage conflicts. Intuitively, we may try the best to avoid conflicts when initiating a collaborative activity, because we may save the effort of conflict resolution. However, conflict avoidance is a complex task that requires many efforts. We need to know if we could get a reward from our payment in conflict avoidance. This paper sets up a model to evaluate the collaboration performance before and after conflict avoidance; conducts experiments with simulations; finally conclude the benefits for conflict avoidance. The contribution of this paper is to assert that conflict avoidance is beneficial in most cases unless the conflicts are not severe, there are not many conflicts, and the number of roles is too small.

关键词： Ice

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Computing finite abstractions with robustness margins via local reachable set over-approximation

Computing finite abstractions with robustness margins via lo...

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作者： Li, Yinan Liu, Jun Ozay, Necmiye Department of Automatic Control and System Engineering University of Sheffield SheffieldS1 3JD United Kingdom Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

This paper proposes a method to compute finite abstractions that can be used for synthesizing robust hybrid control strategies for nonlinear systems. Most existing methods for computing finite abstractions utilize some global, analytical function to provide bounds on the reachable sets of nonlinear systems, which can be conservative and lead to spurious transitions in the abstract systems. This problem is even more pronounced in the presence of imperfect measurements and modelling uncertainties, where control synthesis can easily become infeasible due to added spurious transitions. To mitigate this problem, we propose to compute finite abstractions with robustness margins by over-approximating the local reachable sets of nonlinear systems. We do so by linearizing the nonlinear dynamics into linear affine systems and keeping track of the linearization error. It is shown that this approach provides tighter approximations and several numerical examples are used to illustrate of effectiveness of the proposed methods. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Nonlinear systems

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Constrained Model Predictive control of High Dimensional Jump Markov Linear systems

Constrained Model Predictive Control of High Dimensional Jum...

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American control Conference

作者： Jens Tonne Martin Jilg Olaf Stursberg Volkswagen AG Baunatal (Germany) Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel (Germany)

ISBN: (纸本)9781479917730

This paper proposes a model predictive control (MPC) approach for discrete-time jump Markov linear systems (JMLS) considering constraints on the inputs as well as on the expectancy of the states. Prediction equations for the first moment of the states are formulated, in which the dependencies on the inputs, on the expected values of disturbances, and on the current states are directly considered. For the computation of the matrices needed for predicting the first moment of the states, a recursive algorithm is presented. Finally, the prediction equations are used to formulate the MPC problem as a quadratic program (QP). Due to the recursive structure of the prediction equations and the formulation as a QP, the computational effort is low compared to existing approaches. Simulation results demonstrate the properties of the presented MPC approach and its capabilities of controlling large-scale JMLS online.

关键词： Markov processes Mathematical model Optimization Bismuth Probability distribution Predictive control Linear systems

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Event-Based State Estimation for A Class of Nonlinear Discrete-Time Complex Networks with Stochastic Noises

Event-Based State Estimation for A Class of Nonlinear Discre...

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第三十四届中国控制会议

作者： Licheng Wang Zidong Wang Guoliang Wei Yan Song Shanghai Key Lab of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Department of Computer Science Brunel University London

ISBN: (纸本)9781467374439

In this paper, the state estimation problem is investigated for a class of discrete-time complex networks under the eventtriggered framework. The event-based estimator receives the updated measurements from the sensors only when the prespecified event-triggering rule is violated. Compared with the traditional estimator with the clock driven rule, a series of event-based state estimators are developed so as to reduce unnecessary data transmissions in the communication channel. Attention is focused on the analysis and design problem of the event-based estimators for the addressed discrete-time complex networks such that the estimation error is exponentially bounded in mean square. Some sufficient conditions are obtained to ensure the existence of the desired estimators and the upper bound of the estimation error is derived. By using the convex optimization technique,the gain parameters of the desired estimators are obtained in an explicit form. Finally, a numerical example is used to show the effectiveness of the proposed estimation approach.

关键词： Event-triggered mechanism,Complex networks,Nonlinearities,State estimation

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A Simulation Platform for Characteristic Model-Based Adaptive control systems

A Simulation Platform for Characteristic Model-Based Adaptiv...

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第三十四届中国控制会议

作者： Liangren Shi Zongli Lin Yuanlong Li Department of Automation and Key Laboratory of System Control and Information ProcessingShanghai Jiao Tong University Charles L. Brown Department of Electrical and Computer Engineering University of Virginia

ISBN: (纸本)9781467374439

Characteristic modeling and the adaptive control design based on it have attracted increasing attention from the research community due to their many successful practical applications that have been reported. This paper presents a Matlab simulation platform that has been developed to facilitate the study of characteristic model based adaptive control systems. The simulation platform consists of two interweaving components: for a given continuous-time linear time-invariant plant, represented by a transfer function, and a specified sampling period, the simulation platform calculates a discretized model of the plant, and the corresponding characteristic model, and with a specified set of initial conditions of the characteristic model and its coefficients, along with the specified duration of simulation and reference input, the simulation platform will adaptively tune the coefficients of the characteristic model and calculate the outputs of the plant and the characteristic model. All the data needed for the simulation will be entered through a graphic user interface, which also plots the evolution of the outputs of the plant and the characteristic model as well as the evolution of the coefficients of the characteristic model. The simulation results can also be saved for off line plotting and analysis through the graphic user interface.

关键词： Characteristic model,adaptive control,simulation

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RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria

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Natural Hazards 2015年第2期77卷 1055-1068页

作者： Olatomiwa, Lanre Mekhilef, Saad Shamshirband, Shahaboddin Petkovic, Dalibor Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Department of Electrical and Electronic Engineering Federal University of Technology Minna Nigeria Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department for Mechatronics and Control Faculty of Mechanical Engineering University of Niš Nis Serbia

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( $$ \bar{n} $$ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.

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