IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第7页

State Estimation of Stochastic Boolean Networks Based on Event-Triggered Sampling 基于事件触发抽样的随机布尔网络状态估计

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3560648

Zibo Wei;Yong Ding;Yuqian Guo;Weihua Gui

{"title":"State Estimation of Stochastic Boolean Networks Based on Event-Triggered Sampling","authors":"Zibo Wei;Yong Ding;Yuqian Guo;Weihua Gui","doi":"10.1109/TSMC.2025.3560648","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3560648","url":null,"abstract":"A stochastic Boolean network (SBN) emerges as a more realistic model for gene regulatory networks than a deterministic Boolean network (BN). In order to reduce output sampling while ensuring a given estimation accuracy, this article proposes an event-triggered sampling strategy for the state estimation of SBNs. Under this strategy, the output is sampled when the one-step prediction mean error exceeds a prespecified threshold. An iterative algorithm for the state probability distribution is proposed based on the algebraic form of SBNs, which determines the optimal state estimation. A matrix inequality method is proposed to calculate the worst-case mean estimation error based on its monotonicity with time. Then, the range of sampling triggering thresholds that minimize the worst-case mean estimation error is obtained. This article demonstrates that the event-triggered sampling strategy can make a tradeoff between estimation error and sampling rate. It explains that the full sampling estimator is a special event-triggered sampling estimator. Finally, the proposed method is applied to BN models of the lac operon in Escherichia coli to analyze the relationship among the sampling triggering threshold, the sampling rate, and the estimation error.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 7","pages":"4979-4990"},"PeriodicalIF":8.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Output Regulation Based on Zero-Sum Game for Discrete-Time System Driven by Exogenous Signal 基于零和博弈的外源信号驱动离散时间系统输出调节

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3560421

Ruizhuo Song;Gaofu Yang;Frank L. Lewis

{"title":"Output Regulation Based on Zero-Sum Game for Discrete-Time System Driven by Exogenous Signal","authors":"Ruizhuo Song;Gaofu Yang;Frank L. Lewis","doi":"10.1109/TSMC.2025.3560421","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3560421","url":null,"abstract":"This article proposes a novel Q-learning algorithm that relies solely on input-output data to address the output regulation control problem of complex discrete-time systems affected by exogenous signals. Unlike traditional methods, this algorithm does not require detailed system information, state knowledge, or data about external systems or exogenous signals. Additionally, the control strategy does not depend on state information, but on input-output data processed by a set of filters. We provide upper and lower bounds on the discount factor, eliminating the need to solve the Riccati equation. These bounds ensure that the value function remains finite, and we prove the stability of the system when using control inputs derived from the value function with the given discount factor. Furthermore, the Q-learning algorithm, when applied with input data containing probing noise, is shown to yield Q-function estimates that are independent of the probing noise. Finally, a simulation involving a grid-connected inverter is presented, demonstrating the effectiveness of the proposed algorithm in a practical setting.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 7","pages":"5069-5079"},"PeriodicalIF":8.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Fixed-Time Event-Triggered Bipartite Consensus of Multiagent Systems Under Time-Varying Disconnected Topologies 时变非连通拓扑下多智能体系统的固定时间事件触发二部一致性

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3560947

Xiaoyang Liu;Haibin He;Zhuyan Jiang;Lu Fan;Wenwu Yu

引用次数: 0

Finite-Time Synchronization of Complex Dynamic Networks via Pinning Hybrid Control With Stochastic Disturbances 基于随机扰动的钉住混合控制的复杂动态网络有限时间同步

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3559529

Bo Zhang;Liang Chen;Shengli Xie;Feiqi Deng

引用次数: 0

Human Leading Behavior Learning for Multiple Autonomous Followers Under Constrained Communication Topologies 约束通信拓扑下多自治追随者的人类领导行为学习

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3560325

Xiao-Xiao Zhang;Huai-Ning Wu;Jin-Liang Wang

{"title":"Human Leading Behavior Learning for Multiple Autonomous Followers Under Constrained Communication Topologies","authors":"Xiao-Xiao Zhang;Huai-Ning Wu;Jin-Liang Wang","doi":"10.1109/TSMC.2025.3560325","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3560325","url":null,"abstract":"Owing to the immaturity of current artificial intelligence techniques, practical multiagent systems (MASs) often require supervision and intervention from humans. However, it is unrealistic for a human to monitor the entire MAS and provide appropriate input in some circumstances. A viable approach is to allow a human to control an agent as the leader which in turn influences the other autonomous followers. To this end, a critical issue is how to learn human behavior to improve the autonomy of followers for collaborating with human effectively, since the autonomous followers do not have prior knowledge of human behavior. In this article, the human leading behavior learning problem is studied for a class of human-in-the-loop (HiTL) MASs that are not fully connected. A linear quadratic differential game framework is applied to formulate the collaborative control problem in the HiTL MAS where the human behavior is represented as a cost function whose weighting matrix is unknown to the followers. In the HiTL MAS, we select a follower that has strong computing power called follower 1 to learn the human behavior via an online adaptive inverse differential game (IDG) approach. Based on concurrent learning (CL) technique, an adaptive law is developed for follower 1 to determine the human feedback matrix online, and at the same time the interaction strategies for the autonomous followers are also calculated by follower 1 in case of the constrained communication topology. Subsequently, the weighting matrix in the human cost function is recovered by addressing a linear matrix inequality (LMI) optimization problem. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 7","pages":"4791-4803"},"PeriodicalIF":8.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Obstacle Avoidance and Safe Coverage of Moving Domains for Multiagent Systems via Adaptive Control Barrier Function 基于自适应控制障碍函数的多智能体系统避障与运动域安全覆盖

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3561205

Shuxuan Wu;Lijun Long

引用次数: 0

Privacy-Preserving Distributed Estimation Over Sensor Networks With Multistrategy Injection Attacks: A Chaotic Encryption Scheme 多策略注入攻击下传感器网络的隐私保护分布式估计：一种混沌加密方案

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3560404

Lijuan Zha;Jinzhao Miao;Jinliang Liu;Engang Tian;Chen Peng

{"title":"Privacy-Preserving Distributed Estimation Over Sensor Networks With Multistrategy Injection Attacks: A Chaotic Encryption Scheme","authors":"Lijuan Zha;Jinzhao Miao;Jinliang Liu;Engang Tian;Chen Peng","doi":"10.1109/TSMC.2025.3560404","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3560404","url":null,"abstract":"This article explores the distributed set-membership state estimation problem over sensor networks (SNs) with chaotic encrypted privacy-preserving scheme and multistrategy injection attacks (MIAs). Since potential eavesdroppers in communication networks may intercept the transmitted measurement signals, chaotic encryption is adopted as a privacy-preserving scheme to protect the system state information from being revealed. The measurement signals are encrypted before transmission and decrypted upon reception by the remote estimator. A newly devised attack model is developed to characterize the injection attacks, which occur randomly and involve a combination of multiple attack strategies. By employing matrix inequality techniques, a unified set-membership estimation scheme is developed when both the privacy-preserving scheme and the MIAs coexist. Subsequently, based on the sufficient condition of constraining the estimation error within an ellipsoidal range, an optimization problem is formulated to achieve the optimal estimation performance at each time step, along with the development of a recursive algorithm for computing the required estimator parameters. Finally, simulation is provided to verify the set-membership estimation approach under the chaotic encryption scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 7","pages":"4969-4978"},"PeriodicalIF":8.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Competition and Cooperation of Multiagent System for Moving Target Defense With Dynamic Task-Switching 动态任务切换下多智能体运动目标防御系统的竞争与合作

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3561037

Haiyan Zhao;Rongxin Cui;Weisheng Yan;Lepeng Chen

{"title":"Competition and Cooperation of Multiagent System for Moving Target Defense With Dynamic Task-Switching","authors":"Haiyan Zhao;Rongxin Cui;Weisheng Yan;Lepeng Chen","doi":"10.1109/TSMC.2025.3561037","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3561037","url":null,"abstract":"In this study, we present a coordinated protocol for a multiagent system (MAS) in competitive and cooperative manners for moving target defense with dynamic task-switching. The protocol comprises three components. First, we design a distance-based competitive distributed decision algorithm within an improved k-Winner-Take-All (k-WTA) framework. This algorithm generates dynamic binary task-driven signals for each agent, enabling near-optimal online grouping of MAS with arbitrary proportions. Second, we introduce a cooperative strategy that employs a shared decision-making mechanism and utilizes feedback linearization without global position information. This strategy generates motion planning signals to coordinate the agents’ actions, achieving overall cooperative behaviors such as tracking, capturing, and intercepting. Finally, we incorporate an adaptive sliding mode technique based on second-order nonlinear dynamics to enhance robustness against disturbances, ensuring uniformly ultimately boundedness (UUB) of the closed-loop system. In addition, simulations and experiments with wheeled mobile robots (WMRs) validate the effectiveness of our method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 7","pages":"5003-5016"},"PeriodicalIF":8.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Large-Scale Behavioral Three-Way Group Decision Based on Prospect Theory Under Dual Hesitant Fuzzy Environment 双犹豫模糊环境下基于前景理论的大规模行为三方群体决策

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-25 DOI: 10.1109/TSMC.2025.3561051

Wenjing Lei;Xiaona Li;Kaixin Gong;Bingzhen Sun

{"title":"Large-Scale Behavioral Three-Way Group Decision Based on Prospect Theory Under Dual Hesitant Fuzzy Environment","authors":"Wenjing Lei;Xiaona Li;Kaixin Gong;Bingzhen Sun","doi":"10.1109/TSMC.2025.3561051","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3561051","url":null,"abstract":"Three-way decision (TWD) is a powerful method to tackle complex and high-risk decision-making problems. The proliferation of social networks and rapid exchange of information has fostered growing instances of large-scale group decision-making (LSGDM), which has become an emerging concern because of its necessity and applicability in distinct domains. This necessitates the investigation of LSGDM within the framework of TWD. Given the essential role of the consensus on loss functions in three-way group decision (TWGD) and the significant impact of bounded rationality on decision results, this article, therefore, aims to present a consensus-reaching process (CRP) model for the prospect theory-based utility functions in LSGDM and thus build a novel large scale behavioral TWGD model for solving LSGDM issues under dual hesitant fuzzy (DHF) uncertainty. Specifically, the behavioral DHF TWD based on prospect theory is first built, wherein the threshold parameters are figured out by maximizing expected prospect value, instead of minimizing expected loss. Then, the social network based on social degree considering the co-opetition relationship is constructed, and a social network analysis method is implemented to cluster decision-makers. Accordingly, the CRP model for the prospect value adjustment is designed. Afterward, a large-scale behavioral TWGD model is proposed to tackle an LSGDM issue, resilient supplier selection. Finally, a numerical example and comparative analysis are conducted to demonstrate the effectiveness of the proposed model.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 7","pages":"4943-4956"},"PeriodicalIF":8.6,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A Model-Free Stealthy Attack for Cyber-Physical Systems Based on Deep Reinforcement Learning 基于深度强化学习的网络物理系统无模型隐身攻击

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-23 DOI: 10.1109/TSMC.2025.3559710

Qirui Zhang;Siqi Meng;Wei Dai;Zhenxing Xia;Chunyu Yang;Xuesong Wang

引用次数: 0