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An iterative Q-learning based global consensus of discrete-time saturated multi-agent systems

CHAOS 2019年第10期29卷 103127-103127页

作者： Long, Mingkang Su, Housheng Wang, Xiaoling Jiang, Guo-Ping Wang, Xiaofan Huazhong Univ Sci & Technol Educ Minist China Image Proc & Intelligent Control Key Lab Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210023 Jiangsu Peoples R China Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Jiangsu Peoples R China Shanghai Univ Dept Automat Shanghai 200072 Peoples R China

This paper addresses the consensus problem of discrete-time multiagent systems (DTMASs), which are subject to input saturation and lack of the information of agent dynamics. In the previous works, the DTMASs with input saturation can achieve semiglobal consensus by utilizing the low gain feedback (LGF) method, but computing the LGF matrices by solving the modified algebraic Riccati equation requires the knowledge of agent dynamics. In this paper, motivated by the reinforcement learning method, we propose a model-free Q-learning algorithm to obtain the LGF matrices for the DTMASs achieving global consensus. Firstly, we define a Q-learning function and deduce a Q-learning Bellman equation, whose solution can work out the LGF matrix. Then, we develop an iterative Q-learning algorithm to obtain the LGF matrix without the requirement of the knowledge about agent dynamics. Moreover, the DTMASs can achieve global consensus. Lastly, some simulation results are proposed to validate the effectiveness of the Q-learning algorithm and show the effect on the rate of convergence from the initial states of agents and the input saturation limit. Published under license by AIP Publishing.

关键词： discrete time systems iterative methods matrix algebra multi-agent systems nonlinear dynamical systems Riccati equations

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Sample Weighted Gradient Regularization for Source-Free Domain Adaptation

SSRN

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SSRN 2024年

作者： Long, Yunchong Xu, Bingrong Zhang, Qingyong Yin, Jianhua Zeng, Zhigang School of Automation Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China The Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

Conventional Domain Adaptation(DA) algorithms typically rely on access to the source data,which may not always be practical due to privacy concerns or limited storage *** address these challenges,Source-Free Domain Adaptation(SFDA) has emerged as an alternative that relies solely on a pre-trained source model rather than the actual source *** SFDA,pseudo-label supervised learning is commonly employed and initially plays a significant role in enhancing model performance during the early stages of target domain ***,as adaptation progresses,the performance can stagnate or even deteriorate due to the noise inherent in pseudo-labels,a phenomenon known as Confirmation *** noise present in target domain pseudo-labels introduces additional uncertainty and overfitting risks to the *** this study,a novel framework named Sample Weighted Gradient Regularization (SWGR) was introduced to tackle this *** first propose the label Uncertainty Estimation Metric (LUEM),a reweighting strategy that assesses the reliability of *** metric evaluates label uncertainty from both the source domain pre-trained model and spatial distribution of target domain samples,allowing the model to achieve improved cross-domain transfer performance with high-confidence samples in the target domain. Additionally,we impose a constraint on the gradient descent process of the total loss function in the form of an inequality to prevent entropy from *** Gradient Regularization eliminates overfitting while also guiding the model in lowering prediction uncertainty *** approach reduces the uncertainty in the target domain and enhances the model's generalization *** results demonstrate that our method surpasses competing SFDA approaches and even outperforms many traditional DA methods which require extensive amounts of source domain data. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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-Gain analysis of event-triggered networked control systems: a discontinuous Lyapunov functional approach

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International Journal of Robust and Nonlinear control 2012年第11期23卷

作者： Songlin Hu Dong Yue Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

In this paper, the problems of exponential stability and -gain analysis of event-triggered networked control systems (NCSs) with network-induced delays are studied. We first propose event-triggering conditions in the sensor side and controller side, respectively. Because the implementation of our event-triggering scheme only needs periodic supervision of the system state at the constant sampling instants, instead of being monitored continuously, it is expected that the scheme will improve the resource utilization. Taking the network-induced delays into account and using delay system approach, we constructed a unified model of NCSs with hybrid event-triggering schemes. On the basis of this model, sufficient conditions for the exponential stability and -gain analysis are developed in the form of LMIs by using a discontinuous Lyapunov–Krasovskii functional approach. Moreover, the corresponding results can be further extended to more general cases, where the system matrices of the considered plant contain parameter uncertainties, represented in either polytopic or norm-bounded frameworks. In addition, as a special case, we also present the exponential stability, -gain analysis, and the control feedback gain design of event-triggered NCSs without considering the effects of network-induced delays and event-triggering condition in the controller side. Finally, a simulation example is provided to illustrate the usefulness and effectiveness of the proposed hybrid event-triggering *** © 2012 John Wiley & Sons, Ltd.

关键词： event-triggering exponential stability -gain Lyapunov–Krasovskii functional NCSs

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Optimal Fault Detection Observer Design using Excluding Degree

Optimal Fault Detection Observer Design using Excluding Degr...

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IEEE Conference on Decision and control

作者： Feng Xu Yiming Wan Ye Wang Division of Information Science and Technology Shenzhen International Graduate School Tsinghua University Shenzhen P.R. China School of Artificial Intelligence and Automation Huazhong University of Science and Technology and Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan P.R. China Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC Australia

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a novel optimal gain design method for robust fault detection using set-valued observers. First, a new fault detection performance specification is proposed for set-valued observers based on a new notion named the excluding degree of the origin from a zonotope. Second, a fractional programming problem is formulated to describe fault detection optimality of set-valued observers. Third, optimal fault detection gains are obtained by solving the fractional programming problem. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

关键词： Fault detection Design methodology Observers Programming Computational complexity Optimization Numerical stability

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Hierarchical P systems with randomized right-hand sides of rules 18th

Hierarchical P systems with randomized right-hand sides of r...

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18th International Conference on Membrane Computing, CMC 1018

作者： Alhazov, Artiom Freund, Rudolf Ivanov, Sergiu Institute of Mathematics and Computer Science Academy of Sciences of Moldova Academiei 5 Chişinău2028 Moldova Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China Faculty of Informatics TU Wien Favoritenstraße 9–11 Vienna1040 Austria LACL Université Paris Est – Créteil Val de Marne 61 av. Général de Gaulle Créteil94010 France TIMC-IMAG/DyCTiM Faculty of Medicine of Grenoble 5 avenue du Grand Sablon La Tronche38700 France

ISBN: (纸本)9783319733586

P systems are a model of hierarchically compartmentalized multiset rewriting. We introduce a novel kind of P systems in which rules are dynamically constructed in each step by non-deterministic pairing of left-hand and right-hand sides. We define three variants of right-hand side randomization and compare each of them with the power of conventional P systems. It turns out that all three variants enable non-cooperative P systems to generate exponential (and thus non-semi-linear) number languages. We also give a binary normal form for one of the variants of P systems with randomized rule right-hand sides. © 2018, Springer International Publishing AG.

关键词： Hierarchical systems

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

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International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

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Temperature control for Nano-Scale Films by Spatially-Separated Atomic Layer Deposition Based on Generalized Predictive control

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IEEE Transactions on Nanotechnology 2015年第6期14卷 1094-1103页

作者： He, Wen-Jie Zhang, Hai-Tao Chen, Zhiyong Lin, Ji-Long Tian, Kan Shan, Bin Chen, Rong State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper established a closed-loop temperature control system for a spatially-separated atomic layer deposition (S-ALD) reactor using generalized predictive control (GPC) algorithm. The GPC-based closed-loop control system rapidly and precisely stabilized the reactor temperature in the presence of thermal field disturbances. Compared with the proportion-integration differentiation (PID) control commonly used for S-ALD, the closed-loop GPC system attenuated reaction temperature oscillations when producing two-element nano-scale thin films. Furthermore, the proposed GPC system demonstrated the superiority in multi-element nano-laminates depositing efficiency by reducing the settling time with a substrate moving between different reactors. Electrical and optical properties of films verified the feasibility of the proposed GPC system. Finally, experimental results presented that the microstructure of the deposited ALD nanometer thin film was improved with the developed GPC system, compared with the PID strategy. © 2002-2012 IEEE.

关键词： Atomic layer deposition

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Languages generated by numerical P systems with thresholds

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THEORETICAL COMPUTER SCIENCE 2024年 988卷

作者： Zhang, Luping Xu, Fei East China Univ Technol Jiangxi Engn Technol Res Ctr Nucl Geosci Data Sci Sch Informat Engn Jiangxi Engn Lab Radioact Geosci & Big Data Techno Nanchang 330013 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

Numerical P systems with thresholds (NPT systems) are a type of distributive and parallel computing model in the framework of membrane computing, inspired by the mechanism of information processing in cells. In membrane computing, the study of language generating/accepting power of a computing model is essential for understanding its capabilities and limitations, while the language generating/accepting power of NPT systems remains open. In this work, a language hierarchy for NPT systems is established by considering polynomials of degree one as production functions. Specifically, if each production function is a variable with a positive integer coefficient, the NPT systems characterize regular languages. If each production function is a variable with an integer coefficient, the languages generated by the NPT systems are no more than context -sensitive. Further, the NPT systems generate all recursively enumerable languages if each production function is a linear combination of no more than two variables and each term of a production function is a variable with an integer coefficient. The results show that the language generating power of NPT systems is closely related to the number of variables and the range of coefficients in production functions.

关键词： Bio-inspired computing Membrane computing Numerical P system Language

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Native gating behavior of ion channels in neurons with null-deviation modeling

Native gating behavior of ion channels in neurons with null-...

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The 4th International Ion Channel Conference(第四届国际离子通道会议)

作者： Wei Wang Jie Luo Panpan Hou Yimei Yang Feng Xiao Ming Yuchi Anlian Qu Luyang Wang Jiuping Ding Key Laboratory of Molecular Biophysics of the Ministry of Education College of Life Science and Technology Huazhong University of Science and Technology Wuhan China program in Neurosciences and Mental Health SickKids Research Institute & Department of Physiology University of Toronto Toronto Canada Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Department of Biomedical Engineering College of Life Science and Technology Wuhan China

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Natural grasp intention recognition based on gaze fixation in human-robot interaction

arXiv

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arXiv 2020年

作者： Yang, Bo Huang, Jian Li, Xiaolong Chen, Xinxing Xiong, Caihua Hasegawa, Yasuhisa Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Mechanical Science and Engineering The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Micro-Nano Mechanical Secience and Engineering Nagoya University Furo-cho Chikusa-ku Nagoya464-8603 Japan

Eye movement is closely related to limb actions, so it can be used to infer movement intentions. More importantly, in some cases, eye movement is the only way for paralyzed and impaired patients with severe movement disorders to communicate and interact with the environment. Despite this, eye-tracking technology still has very limited application scenarios as an intention recognition method. The goal of this paper is to achieve a natural fixation-based grasping intention recognition method, with which a user with hand movement disorders can intuitively express what tasks he/she wants to do by directly looking at the object of interest. Toward this goal, we design experiments to study the relationships of fixations in different tasks. We propose some quantitative features from these relationships and analyze them statistically. Then we design a natural method for grasping intention recognition. The experimental results prove that the accuracy of the proposed method for the grasping intention recognition exceeds 89% on the training objects. When this method is extendedly applied to objects not included in the training set, the average accuracy exceeds 85%. The grasping experiment in the actual environment verifies the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Eye movements

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