IEEE Transactions on Signal and Information Processing over Networks最新文献_第8页

Optimizing Multi-Agent Systems With Uncertain Dynamics: A Finite-Time Adaptive Distributed Approach 优化动态不确定的多代理系统：有限时间自适应分布式方法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-01 DOI: 10.1109/TSIPN.2023.3338467

Jiayi Lei;Yuan-Xin Li;Choon Ki Ahn

{"title":"Optimizing Multi-Agent Systems With Uncertain Dynamics: A Finite-Time Adaptive Distributed Approach","authors":"Jiayi Lei;Yuan-Xin Li;Choon Ki Ahn","doi":"10.1109/TSIPN.2023.3338467","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3338467","url":null,"abstract":"The topic of this study is adaptive distributed finite-time (FT) optimization of uncertain nonlinear high-order multi-agent systems (MASs) with disturbances. The proposed two-stage framework consists of an optimal FT estimator and an adaptive FT tracking controller. First, the estimator drives the optimization variables towards the optimal solution. In contrast to existing optimization control studies, high-order MASs subject to unknown dynamics are studied in this case. Second, by using the output of the estimator as a reference signal, the tracking controller allows all agents to approach the optimal point. The use of a command filter avoids the problem of discontinuous gradient functions, while it is possible to handle unknown nonlinear functions using fuzzy logic systems (FLSs). We prove, based on the FT stability criterion and convex optimization theory, that the proposed strategy minimizes the total objective function and results in a closed-loop system with bounded signals and FT convergence to the optimal solution. Finally, through a simulation example, the developed approach is verified.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"865-874"},"PeriodicalIF":3.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exponential Synchronization of Reaction-Diffusion Systems on Networks via Asynchronous Intermittent Control 通过异步间歇控制实现网络上反应扩散系统的指数同步

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-01 DOI: 10.1109/TSIPN.2023.3338452

Jian Liu;Yan Yang;Yongbao Wu;Seaar Al-Dabooni;Lei Xue;Donald C. Wunsch

引用次数: 0

A Closed-Form Solution for Graph Signal Separation Based on Smoothness 基于平滑性的图形信号分离闭式解法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-11-28 DOI: 10.1109/TSIPN.2023.3335615

Mohammad-Hassan Ahmad Yarandi;Massoud Babaie-Zadeh

引用次数: 0

Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions 具有传感器分辨率的多速率非线性系统基于编解码的分布式融合滤波

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-11-23 DOI: 10.1109/TSIPN.2023.3334496

Jun Hu;Shuting Fan;Cai Chen;Hongjian Liu;Xiaojian Yi

{"title":"Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions","authors":"Jun Hu;Shuting Fan;Cai Chen;Hongjian Liu;Xiaojian Yi","doi":"10.1109/TSIPN.2023.3334496","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3334496","url":null,"abstract":"The paper investigates the distributed fusion filtering problem for time-varying multi-rate nonlinear systems (TVMRNSs) with sensor resolutions based on the encoding-decoding scheme (EDS) over sensor networks, where the iterative method is applied to the transformation of TVMRNSs. In order to enhance signal interference-resistant capability and improve transmission efficiency, the EDS based on dynamic quantization is introduced during the measurement transmission. On the basis of the decoded measurements, a local distributed filter is constructed, where an upper bound on the local filtering error (LFE) covariance is derived and the local filter gains are obtained by minimizing the trace of the upper bound. Subsequently, the fusion filtering algorithm is presented according to the covariance intersection fusion criterion. In addition, a sufficient condition is provided via reasonable assumptions to ensure the uniform boundedness of the upper bound on the LFE covariance. Finally, a moving target tracking practical example is taken to show the superiority of the proposed filtering algorithm and discuss the monotonicity of the mean-square error of the fusion filter with respect to the sensor resolutions and quantization intervals.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"811-822"},"PeriodicalIF":3.2,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138472866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Estimation by Partial Sensor Measurements Through Transmission Scheduling for Stochastic Systems 随机系统传输调度中部分传感器测量的分布估计

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-11-06 DOI: 10.1109/TSIPN.2023.3329301

Yun Chen;Yuhang Jin;Jianjun Bai;Mengze Zhu

引用次数: 0

Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach 自组织机器人网络拓扑可恢复性预测:一种数据驱动的容错方法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-10-30 DOI: 10.1109/TSIPN.2023.3328275

Matin Macktoobian;Zhan Shu;Qing Zhao

引用次数: 0

Practical Fixed-Time Consensus Tracking for Second-Order Multi-Agent Systems With Mismatched Disturbances 具有不匹配扰动的二阶多智能体系统的实用定时一致性跟踪

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-10-27 DOI: 10.1109/TSIPN.2023.3328276

Jiayi Gong;Fuyong Wang;Zhongxin Liu;Zengqiang Chen

引用次数: 0

Personalized Graph Federated Learning With Differential Privacy 具有差分隐私的个性化图联邦学习

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-10-23 DOI: 10.1109/TSIPN.2023.3325963

Francois Gauthier;Vinay Chakravarthi Gogineni;Stefan Werner;Yih-Fang Huang;Anthony Kuh

{"title":"Personalized Graph Federated Learning With Differential Privacy","authors":"Francois Gauthier;Vinay Chakravarthi Gogineni;Stefan Werner;Yih-Fang Huang;Anthony Kuh","doi":"10.1109/TSIPN.2023.3325963","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3325963","url":null,"abstract":"This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among different models to provide a more relevant experience for each device, even in situations with diverse data distributions and disproportionate datasets. Furthermore, to ensure a secure and efficient approach to collaborative personalized learning, we study a variant of the PGFL implementation that utilizes differential privacy, specifically zero-concentrated differential privacy, where a noise sequence perturbs model exchanges. Our mathematical analysis shows that the proposed privacy-preserving PGFL algorithm converges to the optimal cluster-specific solution for each cluster in linear time. It also reveals that exploiting similarities among clusters could lead to an alternative output whose distance to the original solution is bounded and that this bound can be adjusted by modifying the algorithm's hyperparameters. Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy. Finally, the effectiveness of the proposed PGFL algorithm is showcased through numerical experiments conducted in the context of regression and classification tasks using some of the National Institute of Standards and Technology's (NIST's) datasets, namely, MNIST, and MedMNIST.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"736-749"},"PeriodicalIF":3.2,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109157459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leader-Following Containment Control of Hybrid Fractional-Order Networked Agents With Nonuniform Time Delays 非均匀时滞混合分数阶网络智能体的leader - follow约束控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-10-23 DOI: 10.1109/TSIPN.2023.3325967

Weihao Li;Lei Shi;Mengji Shi;Jiangfeng Yue;Boxian Lin;Kaiyu Qin

{"title":"Leader-Following Containment Control of Hybrid Fractional-Order Networked Agents With Nonuniform Time Delays","authors":"Weihao Li;Lei Shi;Mengji Shi;Jiangfeng Yue;Boxian Lin;Kaiyu Qin","doi":"10.1109/TSIPN.2023.3325967","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3325967","url":null,"abstract":"Time delays, such as transmission delays or measurement delays, are common phenomena in practical networked control systems. These delays directly threaten the effective completion of cooperative tasks. In this study, the leader-following containment control problem of hybrid fractional-order networked agents with nonuniform time delays is addressed. The position and velocity loops of each double-integrator agent are modeled by fractional-order calculus equations of different orders, which is also called the hybrid fractional-order networked agent system. At first, the mathematical expressions for the upper bound of allowable time delays with respect to the system parameters, such as fractional order, topological structure properties, and controller gains, are given explicitly considering both the directed and undirected graph conditions. Then, this paper obtains the maximum allowable upper bounds of time delays for achieving leader-following containment tracking control in the case of fractional order mismatch. Based on this, it is convenient to calculate the delay margin directly and to judge the stability of the networked agent systems with nonuniform time delays. Finally, some simulation results are given to verify the effectiveness of the delay margin for networked agent systems. The results show that the system stability can be directly judged by calculating the critical time delay condition; meanwhile, the system robustness can also be improved by actively adjusting the controller parameters to increase the delay margin.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"750-760"},"PeriodicalIF":3.2,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109157520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Noise Resilient Distributed Average Consensus Over Directed Graphs 有向图上噪声弹性分布平均一致性

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-10-16 DOI: 10.1109/TSIPN.2023.3324583

Vivek Khatana;Murti V. Salapaka

{"title":"Noise Resilient Distributed Average Consensus Over Directed Graphs","authors":"Vivek Khatana;Murti V. Salapaka","doi":"10.1109/TSIPN.2023.3324583","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3324583","url":null,"abstract":"Motivated by the needs of resiliency, scalability, and plug-and-play operation, distributed decision making is becoming increasingly prevalent. The problem of achieving consensus in a multi-agent system is at the core of distributed decision making. In this article, we study the problem of achieving average consensus over a \u0000<italic>directed</i>\u0000 multi-agent network when the communication links are corrupted with \u0000<italic>noise</i>\u0000. We propose an algorithm where each agent updates its estimates based on the local mixture of information and adds its weighted noise-free initial information to its updates during every iteration. We demonstrate that, with appropriately designed weights, the agents achieve consensus despite additive communication noise. We establish that when the communication links are \u0000<italic>noiseless</i>\u0000, the proposed algorithm moves towards consensus at a geometric rate. Under communication noise, we prove that the agent estimates reach a consensus value \u0000<italic>almost surely</i>\u0000. We present numerical experiments to corroborate the efficacy of the proposed algorithm under different noise realizations and various algorithm parameters.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"770-785"},"PeriodicalIF":3.2,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109157521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0