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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是301-310 订阅

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ILC for a Class of Linear Parabolic DPSs with Moving Boundaries

ILC for a Class of Linear Parabolic DPSs with Moving Boundar...

引用

data driven control and learning systems (ddcls)

作者： Hai Zhang Xisheng Dai Muhammad Shamrooz Aslam Su Wang School of Electrical and Information Engineering Guangxi University of Science and Technology Liuzhou P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this article focuses on the iterative learning control problem of a class of linear parabolic distributed parameter systems, which has the characteristic that the boundaries of the spatial domain change continuously with time. then, the openloop P-type iterative learning method is used to study the system output tracking problem. through rigorous theoretical analysis, some methods such as the contraction mapping approach and Bellman-Gronwall inequality are used to prove the convergence of the tracking error. Finally, the effectiveness of the algorithm is verified by numerical simulation.

关键词： Distributed parameter systems Iterative learning control Convergence Process control Boundary conditions learning systems Mathematical model

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learning rule with fractional-order average momentum based on Tustin generating function for convolution neural networks

Learning rule with fractional-order average momentum based o...

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data driven control and learning systems (ddcls)

作者： Jing Jian Zhe Gao Tao Kan School of Mathematics Liaoning University Shenyang College of Light Industry Liaoning University Shenyang

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, we propose a fractional-order average momentum (FOAM) method based on Tustin generating function to train parameters in convolution neural networks. Taking the classical data set MNIST as the training and testing data, the effectiveness of the FOAM for CNNs is verified. the experimental results show that the stochastic gradient descent method based on FOAM can improve the recognition accuracy and learning convergence speed of convolution neural networks.

关键词： Mathematical model Neural networks Feature extraction Training Optimization Convolution Brain modeling

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Hierarchical least squares based iterative algorithms for two-input single-output Hammerstein finite impulse response systems

Hierarchical least squares based iterative algorithms for tw...

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data driven control and learning systems (ddcls)

作者： Liangbin Sha Yan Ji School of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper focuses on the identification problem for finite impulse response systems through using the hierarchical identification principle. Based on the hierarchical identification principle, the hierarchical based least squares iterative algorithm is proposed to estimate the parameters of the two-input single-output Hammerstein finite impulse response systems. Finally, a simulation example is given to test the effectiveness of the proposed algorithm.

关键词： Nonlinear systems Estimation error Parameter estimation Mathematical model Iterative algorithms data models control systems

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Leveraging machine learning for fake news detection 9

Leveraging machine learning for fake news detection

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9th International conference on data Science, Technology and Applications, data 2020

作者： Masciari, Elio Moscato, Vincenzo Picariello, Antonio Sperlì, Giancarlo University Federico II Naples Italy

ISBN: (纸本)9789897584404

the uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. this problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features (Vosoughi et al., 2018). In this paper, we present our complete framework for fake news detection and we discuss in detail a solution based on machine learning. Our experiments conducted on two well-known and widely used real-world datasets suggest that our settings can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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Adaptive NN Finite-Time Tracking control for PMSM with Full State Constraints

Adaptive NN Finite-Time Tracking Control for PMSM with Full ...

引用

data driven control and learning systems (ddcls)

作者： Jin-Zi Yang Yuan-Xin Li College of Information with the College of Science Liaoning University of Technology Jinzhou China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper proposes a finite-time adaptive tracking control scheme for permanent magnet synchronous motors (PMSM) with full state constraints. In order to deal with full state constraints, the nonlinear transformation function is first introduced to transform the constraint problem into a non-constraint problem. then, we will provide a tracking differentiator that get the differentiation of the virtual control laws. At the same time, the neural networks (NN) are employed to approximate unknown nonlinearities. Finally, simulation results are given to validate the proposed control scheme.

关键词： Time-varying systems Stability criteria Nonlinear systems Backstepping Artificial neural networks Voltage control Transforms

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Iteration-dependent High-order Internal Model based Iterative learning control for Continuous-time Nonlinear systems

Iteration-dependent High-order Internal Model based Iterativ...

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data driven control and learning systems (ddcls)

作者： Miao Yu Sheng Chai College of Electrical Engineering Zhejiang University Hangzhou

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, an adaptive iterative learning control (AILC) scheme based on high-order internal model (HOIM) is presented for a class of nonlinear continuous-time systems with unknown time-iteration-varying parameter. the time-iteration-varying parameter is generated by a general iteration-dependent HOIM with iteration-varying order and coefficients. Compared with the existing works based on iteration-invariant HOIM with fixed order and coefficients, our work significantly expands the application scope of HOIM-based ILC. Using the designed HOIM-based iterative learning controller, the learning convergence along the iteration axis is guaranteed through rigorous theoretical analysis under Lyapunov theory. Furthermore, the effectiveness of the proposed method is demonstrated according to the simulation results.

关键词： Trajectory Nonlinear systems Convergence Iterative learning control data models Correlation control systems

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Sensing and Automation in the Future Maritime Environment 9

Sensing and Automation in the Future Maritime Environment

引用

9th conference of the Sensor-Signal-Processing-for-Defence (SSPD)

作者： Sternlicht, Daniel D. Naval Surface Warfare Ctr Panama City Div 110 Vernon Ave Panama City FL USA

ISBN: (纸本)9781728138107

In this emerging era of great power competition, the goal of outpacing potential adversaries in the development of military technology takes on a new urgency. Evolving capabilities in sensing and automation are driven by a trade space that includes range and lethality versus close engagement and survivability;finders versus hiders;centralized command/control versus asset independence/dispersion;and planning and judgement versus reaction and autonomy. this paper explores this trade space first by describing sensing and automation innovations demonstrated during the 1990/1991 Gulf War and shortly thereafter, followed by discussion on current and emerging game-changing technologies. Capabilities projected for near and far term advantage include: weapons systems ensuring long-range lethality;unmanned cooperative networks of offboard systems;artificial intelligence and machine learning;and exploitation of advanced materials and quantum technologies. these will play a vital role in realizing a networked force of manned and unmanned systems with the ability to sense, comprehend, communicate, predict, plan. and take appropriate action in the future maritime environment.

关键词： Sensors Surveillance Reconnaissance Navigation Communications Automation Autonomy data Analytics Artificial Intelligence Machine learning Advanced Materials Quantum Technologies Gulf War

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Observer-based Optimal Adaptive control for Multi-motor Driving Servo System

Observer-based Optimal Adaptive Control for Multi-motor Driv...

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data driven control and learning systems (ddcls)

作者： Shuangyi Hu Xuemei Ren School of Automation Beijing Institute of Technology Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, an improved optimal sliding mode control strategy is proposed for multi-motor driving servo system. Some states of multi-motor drive system are not measurable and there exists unknown nonlinearity. To solve this problem, the disturbance observer and extended state observer are both applied to estimate the unknown states and nonlinearity. Based on optimal control theory, the optimal sliding surface is selected to guarantee the optimal dynamic performance of the sliding mode of the system. the effectiveness of designed control methods is illustrated by simulation results.

关键词： Disturbance observers Performance analysis Torque Servomotors Sliding mode control Simulation Perturbation methods

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data-driven-Based Distributed Security control for Vehicle-Following Platoon

Data-Driven-Based Distributed Security Control for Vehicle-F...

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data driven control and learning systems (ddcls)

作者： Weiwei Che Chao Deng Dan Liu Institute of Complexity Science Qingdao University Qingdao P. R. China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper proposes a data-driven-based distributed security control approach for vehicle-following platoons in the presence of denial-of-service attacks. First, distributed resilient observers are designed to estimate the trajectories of the leader-vehicle. Based on the developed observers, data-driven controllers are proposed for the vehicle-followers. Specifically, a data-driven approach is introduced to identify controller parameters without relying on the knowledge of system dynamics. Finally, a vehicle-following platoon example is provided to demonstrate the effectiveness of the data-driven-based distributed security control method.

关键词： Security Observers Denial-of-service attack Vehicle dynamics Mathematical model Information processing Trajectory

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A Working and Verification Framework of Marine BDS High Precision Positioning System

A Working and Verification Framework of Marine BDS High Prec...

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data driven control and learning systems (ddcls)

作者： Jieru Niu Lichun Yang Xin Song Jiangsu Automation Research Institute Lianyungang

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

BDS high precision navigation and positioning system plays an important role in marine economy with the "B&R" strategy. this paper focus on marine high precision positioning system based on BDS improvement technology, communication network design and surface/under water precision positioning technology, design the marine BDS high precision positioning test and verification system. An experiment is designed to verify that the system can effectively improve the positioning accuracy of the Beidou system.

关键词： data processing Real-time systems Satellites Base stations Mathematical model Monitoring Maintenance engineering

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