IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第5页

Simultaneous Feature and Instance Selection via Evolutionary Auxiliary Optimization 基于进化辅助优化的特征和实例同步选择

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-04-02 DOI: 10.1109/TSMC.2026.3677086

Ruwang Jiao;Bing Xue;Mengjie Zhang

{"title":"Simultaneous Feature and Instance Selection via Evolutionary Auxiliary Optimization","authors":"Ruwang Jiao;Bing Xue;Mengjie Zhang","doi":"10.1109/TSMC.2026.3677086","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3677086","url":null,"abstract":"In the big data era, the large volume and high dimensionality of data challenge many data mining algorithms. Since many classification algorithms are sensitive to data distribution, removing redundant or noisy features and instances can greatly improve their performance. Feature selection and instance selection are two major data reduction techniques that are inherently interconnected. This article proposes an auxiliary-optimization-assisted constrained multiobjective optimization method that concurrently tackles feature and instance selection, with two key constraints: forcing the obtained subsets to have better classification performance than that of using all features and instances under the given classification algorithm, and bounding the worst-class error. A simple but effective initialization method is designed to sample initial solutions relatively uniformly across different regions of the objective space, to provide a diverse and high-quality set of initial feature and instance subsets. An auxiliary-optimization-based search approach is proposed to fully utilize useful infeasible solutions, which can further reduce the number of selected features and instances without compromising classification performance. The proposed method is compared with a number of promising methods on 20 real-world classification datasets, and the experimental results show that it is generally better than those methods. Additionally, the proposed method offers a significant advantage whereby the majority of its solutions exhibit superior or comparable classification performance compared to using all original features and instances, while selecting no more than 30% of the features and 50% of the training instances across most datasets.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4398-4411"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442748","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

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-06-19 DOI: 10.1109/TSMC.2026.3700368

引用次数: 0

CEC-FedISDG: A Cloud-Edge Collaboration Federated Invariance and Specificity Domain Generalization Method for Machine Remaining Useful Life Prediction 机器剩余使用寿命预测的一种云边缘协作联邦不变性和特异域泛化方法

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-02-25 DOI: 10.1109/TSMC.2026.3663567

Jie Shang;Danyang Xu;Haobo Qiu;Chen Jiang;Liang Gao;Enrico Zio

{"title":"CEC-FedISDG: A Cloud-Edge Collaboration Federated Invariance and Specificity Domain Generalization Method for Machine Remaining Useful Life Prediction","authors":"Jie Shang;Danyang Xu;Haobo Qiu;Chen Jiang;Liang Gao;Enrico Zio","doi":"10.1109/TSMC.2026.3663567","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3663567","url":null,"abstract":"Advances in sensor technology and the Industrial Internet of Things (IIoT) have enabled the collection of large-scale monitoring data, facilitating intelligent remaining useful life (RUL) prediction for industrial equipment. However, accurate RUL prediction in distributed environments faces two major challenges. First, the scarcity of data and limited computational resources at edge clients hinder the development of robust RUL models, while privacy constraints prohibit centralized data sharing. Second, distribution shifts across client machines severely limit the model’s ability to generalize to unknown operating conditions (OCs) and equipment. To address these challenges, this article proposes a cloud-edge collaboration (CEC) federated invariance and specificity domain generalization (DG) (CEC-FedISDG) method. CEC-FedISDG integrates both domain-invariant and domain-specific predictive knowledge within a unified cloud-edge federated learning (FL) framework. This design enables the model to exploit the broad generalizability of invariant features while retaining domain-specific predictive capabilities. Specifically, a progressive invariance refinement (PIR) module is developed to gradually strengthen domain-invariant features while preserving privacy through a two-stage learning process. Subsequently, a dynamic specificity selection (DSS) module is designed. It dynamically integrates the outputs of private-domain regressors that retain domain specificity utilizing a domain classifier, adapting weights to test samples, thereby improving RUL prediction accuracy. Experimental evaluations on two bearing datasets and a real-world industrial wind turbine gearbox (WTG) dataset demonstrate that the CEC-FedISDG achieves superior generalization performance while adhering to strict privacy preservation requirements.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4002-4016"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506927","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

Composite Observer-Based Resilient Adaptive Fault-Tolerant Control for Interconnected Nonlinear Systems Under Deception Attacks 欺骗攻击下基于复合观测器的非线性互联系统弹性自适应容错控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-03-10 DOI: 10.1109/TSMC.2026.3670883

Xinjun Wang;Xinyu Xu;Ben Niu;Xiaomei Wang;Xudong Zhao;Huanqing Wang

{"title":"Composite Observer-Based Resilient Adaptive Fault-Tolerant Control for Interconnected Nonlinear Systems Under Deception Attacks","authors":"Xinjun Wang;Xinyu Xu;Ben Niu;Xiaomei Wang;Xudong Zhao;Huanqing Wang","doi":"10.1109/TSMC.2026.3670883","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3670883","url":null,"abstract":"This article proposes a decentralized resilient adaptive intermittent output feedback fault-tolerant control (FTC) method for interconnected nonlinear systems with sensor deception attacks, multiple actuator faults, and input saturation. First, we constructed a new composite observer using intermittent compromised output signals and fuzzy logic systems (FLSs) to estimate the state variables in the face of deception attacks and unknown external disturbances. On this basis, the reasonable adaptive laws are cleverly devised to handle multiple unpredictable actuator faults. Then, in order to deal with unknown gains caused by deception attacks, a projection operator-based compensation mechanism is proposed. In addition, the “explosion of complexity” in the traditional backstepping design process is solved through command filtering technology. At the same time, an error compensation system and an auxiliary system are designed to reduce the errors caused by command filtering and solve the input saturation problem, respectively. The results indicate that the proposed control scheme can ensure the boundedness of all signals in the closed-loop system, and the tracking errors converge to a bounded compact set. Finally, the effectiveness of the proposed strategy was verified through numerical simulation.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4195-4204"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442471","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

IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-06-19 DOI: 10.1109/TSMC.2026.3700366

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Orthogonal Projected Gradient Differential Neural Solution to Linear and Quadratic Constrained Optimization: Theory and Applications 线性和二次约束优化的正交投影梯度微分神经解：理论与应用

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-02-26 DOI: 10.1109/TSMC.2026.3664485

Ying Liufu;Xin Lv;Long Jin;Shuai Li

引用次数: 0

LEASE: Offline Preference-Based Reinforcement Learning With High Sample Efficiency 基于离线偏好的高样本效率强化学习

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-03-06 DOI: 10.1109/TSMC.2026.3663604

Xiao-Yin Liu;Guotao Li;Xiao-Hu Zhou;Zeng-Guang Hou

{"title":"LEASE: Offline Preference-Based Reinforcement Learning With High Sample Efficiency","authors":"Xiao-Yin Liu;Guotao Li;Xiao-Hu Zhou;Zeng-Guang Hou","doi":"10.1109/TSMC.2026.3663604","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3663604","url":null,"abstract":"Offline preference-based reinforcement learning (PbRL) offers an effective approach to addressing the challenges of designing rewards and mitigating the high costs associated with online interaction. However, since labeling preference needs real-time human feedback, acquiring sufficient preference labels is challenging. To solve this, this article proposes an offline PbRL with a high sample efficiency (<monospace>LEASE</monospace>) algorithm, where a learned transition model is leveraged to generate unlabeled preference data. Considering the pretrained reward model may generate incorrect labels for unlabeled data, we design an uncertainty-aware mechanism to ensure the performance of the reward model, where only high-confidence and low-variance data are selected. Moreover, the generalization bound of the reward model is provided to analyze the factors influencing reward accuracy, and the policy learned by <monospace>LEASE</monospace>has a theoretical improvement guarantee. The above developed theory is based on a state-action pair, which can be easily combined with other offline algorithms. The experimental results show that <monospace>LEASE</monospace>can achieve comparable performance to the baseline under fewer preference data without online interaction.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4017-4029"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506929","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

Reinforcement Learning Optimal Output Feedback Control for Takagi–Sugeno Fuzzy Systems With Disturbances 具有扰动的Takagi-Sugeno模糊系统的强化学习最优输出反馈控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1109/TSMC.2025.3649181

Yifan Deng;Wenting Song;Shaocheng Tong

引用次数: 0

An Auxiliary Problem-Assisted Evolutionary Algorithm With Dynamics Regulation for Complex Constrained Multiobjective Optimization 复杂约束多目标优化的一种带动态调节的辅助问题辅助进化算法

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-02-10 DOI: 10.1109/TSMC.2025.3648724

Zhengpeng Hu;Xiaobing Yu;Junhua Zhu;Gary G. Yen;Yu Xue

{"title":"An Auxiliary Problem-Assisted Evolutionary Algorithm With Dynamics Regulation for Complex Constrained Multiobjective Optimization","authors":"Zhengpeng Hu;Xiaobing Yu;Junhua Zhu;Gary G. Yen;Yu Xue","doi":"10.1109/TSMC.2025.3648724","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3648724","url":null,"abstract":"Existing constrained multiobjective evolutionary algorithms (CMOEAs) frequently employ the information provided by the unconstrained Pareto front (UPF) to facilitate the identification of the constrained Pareto front (CPF) for constrained multiobjective optimization problems (CMOPs). However, obtaining a UPF with favorable convergence and diversity is not straightforward, and the obtained UPF is sometimes difficult to effectively assist in the identification of CPF for certain complex CMOPs. To this end, a novel algorithm called DREMCO is proposed, which endeavors to obtain a good UPF and is capable of utilizing the obtained UPF to consistently assist in the identification of CPF. DREMCO consists of a main population for the original problem and a two-phase (propulsion phase and recovery phase) auxiliary population with a dynamics regulation mechanism. In the propulsion phase, the auxiliary population ignores constraints and employs an improved aggregation function to obtain a good UPF, thereby pulling the main population across infeasible regions. In the recovery phase, the auxiliary population uses a penalty function method to converge to CPF and continuously refines the CPF of the main population. Concurrently, a novel phase judgment method is proposed for seamless transition between phases. Furthermore, an information-sharing strategy is proposed, which is capable of sharing information of the parents and offspring in offspring generation and environment selection, respectively. The experimental results with 11 state-of-the-art CMOEAs on five benchmark suites and eight real-world CMOPs demonstrate the efficacy of the proposed algorithm.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2317-2332"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504712","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 Practical Fixed-Time Intermittent Event-Triggered Control Scheme for Complex Dynamical Networks 一种实用的复杂动态网络固定时间间歇事件触发控制方案

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1109/TSMC.2025.3649923

Xiaofang Hu;Leimin Wang;Feida Song;Chuan-Ke Zhang;Yong He

{"title":"A Practical Fixed-Time Intermittent Event-Triggered Control Scheme for Complex Dynamical Networks","authors":"Xiaofang Hu;Leimin Wang;Feida Song;Chuan-Ke Zhang;Yong He","doi":"10.1109/TSMC.2025.3649923","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649923","url":null,"abstract":"This article investigates the practical fixed-time synchronization (FxTS) for complex dynamical networks (CDNs). Given the notable benefits associated with intermittent control, wherein control inputs are activated solely during the working phase of each control cycle, and event-triggered control, which significantly curtails the frequency of control input updates, this article proposes an intermittent event-triggered control scheme aimed at achieving practical FxTS in CDNs. The proposed scheme substantially mitigates the volume of information transmitted across the network while concurrently reducing control-related expenditures. To mitigate the issue of high-frequency buffeting, the newly devised control scheme employs a saturation function as a substitute for the conventional signum function. Subsequently, this article presents a more precise estimation approach for reasonably approximating the synchronization time (ST) of CDNs and the synchronization error (SE) induced by the integration of the saturation function. Moreover, this article imposes constraints on the frequency and total duration of communication pauses in an average-sense manner and constructs a piecewise Lyapunov function to further deduce several less conservative criteria for practical FxTS. Finally, two illustrative examples are provided to demonstrate the viability of the intermittent event-triggered control scheme and the accuracy of the ST and SE estimations.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2454-2463"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504854","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