IEEE Transactions on Cybernetics最新文献_第7页

IEEE Transactions on Cybernetics

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-06 DOI: 10.1109/TCYB.2025.3543960

引用次数: 0

IEEE Transactions on Cybernetics

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-06 DOI: 10.1109/TCYB.2025.3543962

引用次数: 0

Connect. Support. Inspire.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-06 DOI: 10.1109/TCYB.2025.3546420

引用次数: 0

Medal of Honor

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-06 DOI: 10.1109/TCYB.2025.3546421

引用次数: 0

Predefined Time and Prespecified Precision for Bearing-Constrained AAV Swarm.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-05 DOI: 10.1109/TCYB.2025.3539704

Tao Zhang, Dengxiu Yu, Kang Hao Cheong, Yanjun Liu, Zhen Wang

{"title":"Predefined Time and Prespecified Precision for Bearing-Constrained AAV Swarm.","authors":"Tao Zhang, Dengxiu Yu, Kang Hao Cheong, Yanjun Liu, Zhen Wang","doi":"10.1109/TCYB.2025.3539704","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3539704","url":null,"abstract":"<p><p>This article presents a bearing-based formation control method for autonomous aerial vehicle (AAV) swarms, allowing users to specify both convergence time and precision in advance. Unlike traditional distance-based methods, which rely on intricate distance measurements, our approach simplifies constraints using bearing information, reducing hardware and sensing requirements. It also eliminates the need to update control commands for each AAV, as formation reconfiguration can be achieved solely by adjusting the motion trajectory of formation leaders. Moreover, the strategy demonstrates enhanced robustness in addressing real-world input constraints. A continuous hyperbolic tangent saturation function and an input saturation compensation system are incorporated, ensuring system convergence and precision while addressing singularity issues. In addition, unlike conventional bearing-based strategies focusing primarily on convergence time, the proposed algorithm enables preset control over both convergence time and precision. Finally, the effectiveness of the proposed approach is validated through several illustrative examples, including a 6-degree-of-freedom (6DoF) quadrotor AAV swarm, highlighting its practical applicability and performance.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566932","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

Discounted Inverse Reinforcement Learning for Linear Quadratic Control

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-04 DOI: 10.1109/TCYB.2025.3540967

Han Wu;Qinglei Hu;Jianying Zheng;Fei Dong;Zhenchao Ouyang;Dongyu Li

{"title":"Discounted Inverse Reinforcement Learning for Linear Quadratic Control","authors":"Han Wu;Qinglei Hu;Jianying Zheng;Fei Dong;Zhenchao Ouyang;Dongyu Li","doi":"10.1109/TCYB.2025.3540967","DOIUrl":"10.1109/TCYB.2025.3540967","url":null,"abstract":"Linear quadratic control with unknown value functions and dynamics is extremely challenging, and most of the existing studies have focused on the regulation problem, incapable of dealing with the tracking problem. To solve both linear quadratic regulation and tracking problems for continuous-time systems with unknown value functions, this article develops a discounted inverse reinforcement learning (DIRL) method that inherits the model-independent property of reinforcement learning (RL). More specifically, we first formulate a standard paradigm for solving linear quadratic control using DIRL. To recover the value function and the target control gain, an error metric is elaborately constructed, and a quasi-Newton algorithm is adopted to minimize it. Furthermore, three DIRL algorithms, including model-based, model-free off-policy, and model-free on-policy algorithms, are proposed. The latter two rely on the expert’s demonstration data or the online observed data, requiring no prior knowledge of the system dynamics and value function. The stability, convergence, and existence conditions of multiple solutions are thoroughly analyzed. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 4","pages":"1995-2007"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546738","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 for H ∞ Optimal Control of Unknown Continuous-Time Linear Systems

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-02-28 DOI: 10.1109/tcyb.2025.3541815

Hongyang Li, Qinglai Wei, Xiangmin Tan

引用次数: 0

HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-02-28 DOI: 10.1109/tcyb.2025.3542059

Shuxiang Lin, Chaojie Fan, Demin Han, Ziyu Jia, Yong Peng, Sam Kwong

引用次数: 0

Evolutionary Multitask Optimization for Multiform Feature Selection in Classification

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-02-27 DOI: 10.1109/TCYB.2025.3535722

Qi-Te Yang;Xin-Xin Xu;Zhi-Hui Zhan;Jinghui Zhong;Sam Kwong;Jun Zhang

{"title":"Evolutionary Multitask Optimization for Multiform Feature Selection in Classification","authors":"Qi-Te Yang;Xin-Xin Xu;Zhi-Hui Zhan;Jinghui Zhong;Sam Kwong;Jun Zhang","doi":"10.1109/TCYB.2025.3535722","DOIUrl":"10.1109/TCYB.2025.3535722","url":null,"abstract":"Feature selection (FS) is a significant research topic in machine learning and artificial intelligence, but it becomes complicated in the high dimensional search space due to the vast number of features. Evolutionary computation (EC) has been widely used in solving FS by modeling it as an expensive wrapper-form optimization task, where a classifier is used to obtain classification accuracy for fitness evaluation (FE). In this article, we propose that the FS problem can be also modeled as a cheap filter-form optimization task, where the FE is based on the relevance and redundancy of the selected features. The wrapper-form optimization task is beneficial for classification accuracy while the filter-form optimization task has the strength of a lighter computational cost. Therefore, different from existing multitask-based FS that uses various wrapper-form optimization tasks, this article uses a multiform optimization technique to model the FS problem as a wrapper-form optimization task and a filter-form optimization task simultaneously. An evolutionary multitask FS (EMTFS) algorithm for parallel tacking these two tasks is proposed followed by, in which a two-channel knowledge transfer strategy is proposed to transfer positive knowledge across the two tasks. Experiments on widely used public datasets show that EMTFS can select as few features as possible on the premise of superior classification accuracy than the compared state-of-the-art FS algorithms.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 4","pages":"1673-1686"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518645","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

RCM: A Neural Policy Model With Reconstruction Mechanism to Construct a Solution for the Agile Satellite Scheduling Problem

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-02-25 DOI: 10.1109/TCYB.2025.3535777

Ming Chen;Jie Chun;Witold Pedrycz;Yongming He;Xiaolu Liu;Guohua Wu

{"title":"RCM: A Neural Policy Model With Reconstruction Mechanism to Construct a Solution for the Agile Satellite Scheduling Problem","authors":"Ming Chen;Jie Chun;Witold Pedrycz;Yongming He;Xiaolu Liu;Guohua Wu","doi":"10.1109/TCYB.2025.3535777","DOIUrl":"10.1109/TCYB.2025.3535777","url":null,"abstract":"The agile Earth observation satellite scheduling problem (AEOSSP) with time-dependent transition time is a combinatorial optimization challenge. Due to its NP-hardness, problem-tailored methods are sensitive to instances and require massive computational overhead. Recently, deep reinforcement learning (DRL) models have shown promise in efficiently addressing the AEOSSP. However, these models may make decision mistakes in specific scenarios due to prioritizing maximizing average reward expectation over individual decision accuracy during DRL training, directly leading to resource wastage. To address these issues, we propose a reconstruction model (RCM), which is a DRL-based two-stage construction model (CM), including a CM and a reconstruction mechanism (RM). RCM constructs solutions initially using a DRL-trained CM, which are subsequently refined by RM. CM utilizes a more efficient network for policy representation to make decisions. RM applies two operators, “repair” and “removal,” with a “repair-removal-repair” solution reconstruction process to identify and rectify decision mistakes from CM, offering a modular component to enhance the stability and solution quality. Experimental results demonstrate that the proposed RCM outperforms the state-of-the-art AEOSSP iterative search method, achieving such performance within a computational time of 0.1 s. Additionally, CM surpasses the state-of-the-art DRL policy model and RM can effectively rectify decision errors or suboptimalities, underscoring its effectiveness in enhancing DRL outcomes.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 4","pages":"1941-1953"},"PeriodicalIF":9.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495451","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