IEEE Transactions on Cybernetics最新文献_第8页

SMC for Stirred Tank Reactor Model: Hybrid Systems With Bi-Boundary Sojourn Time 搅拌釜反应器模型的SMC：双边界停留时间混合系统

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-13 DOI: 10.1109/tcyb.2026.3721252

Wenhai Qi, Feiyue Shen, Guangdeng Zong, Shun-Feng Su, Huaicheng Yan

引用次数: 0

Optimal Containment of Multiagent Systems With Multistep Policy Gradient Reinforcement Learning. 基于多步策略梯度强化学习的多智能体系统的最优包容。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-12 DOI: 10.1109/TCYB.2026.3718680

Kaitian Chen, Huaicheng Yan, Qiwei Liu, Lingling Lv, Guangjing Song

引用次数: 0

Reinforcement Learning-Based Optimization for Interval Type-2 Fuzzy Unknown Nonlinear System: An Zero-Sum Control Scheme. 区间2型模糊未知非线性系统的强化学习优化：零和控制方案。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-12 DOI: 10.1109/TCYB.2026.3720445

Runkun Li, Wenhai Qi, Guangdeng Zong, Shun-Feng Su, Jinde Cao

引用次数: 0

An Implementable Compressed Sensing Framework for Gene Expression Profiling via Multiplex PCR. 基于多重PCR的基因表达谱压缩感知框架的实现。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-11 DOI: 10.1109/TCYB.2026.3719420

Yihui Du, Meng Jiang, Songlin Zhuang, Mingsi Tong, Huijun Gao

{"title":"An Implementable Compressed Sensing Framework for Gene Expression Profiling via Multiplex PCR.","authors":"Yihui Du, Meng Jiang, Songlin Zhuang, Mingsi Tong, Huijun Gao","doi":"10.1109/TCYB.2026.3719420","DOIUrl":"10.1109/TCYB.2026.3719420","url":null,"abstract":"Gene expression profiling (GEP) provides critical insights into the cellular states, phenotypes, and biological behaviors of biological systems. However, the traditional GEP acquisition methods have significant time limitations, which directly hinder the real-time monitoring of dynamic processes, such as cell development and disease progression. In this study, we propose the biological omics sparse sensing (BoSS) framework, which effectively resolves the compatibility issue between dimensionality-reduced sampling theory and biological reaction by introducing sparse sampling strategies and noise regularization constraints. Furthermore, we have first presented rapid capture and accurate reconstruction of GEP using multiplex RT-qPCR, increasing the efficiency of transcriptome data acquisition by 60-fold. The experimental results demonstrate that when evaluating transcriptional heterogeneity in cancer tissue blocks, BoSS exhibits excellent specificity and sensitivity in clustering analysis, while fully preserving key information related to biological states, thus ensuring that the accuracy of downstream data analysis is comparable to that of ribonucleic acid (RNA)-seq. This achievement indicates that the BoSS framework is expected to play an important role in scenarios, such as intraoperative assessment of cancer progression, personalized therapy, and predictive control of biological systems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148712189","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 Fault-Tolerant Control for Wastewater Treatment Processes With Asymmetric State Constraints 非对称状态约束下污水处理过程的定时容错控制。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-01 Epub Date: 2026-04-21 DOI: 10.1109/TCYB.2026.3683543

Hong-Gui Han;Han-Qian Hou;Hao-Yuan Sun;Jun-Fei Qiao

引用次数: 0

Interpretable Self-Attention Dynamic Inner Neural Network Model for Nonlinear Dynamic Process Monitoring 非线性动态过程监测的可解释自注意动态内神经网络模型

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-07-31 DOI: 10.1109/tcyb.2026.3716912

Youqing Wang, Haoqian Wang, Tongze Hou, Yiming Dong, Xin Ma

引用次数: 0

Retraction Notice: Multiple Transformation Matrix-Based Adaptive Projective Vortex Formation Tracking for Multiagent Systems With a Leader of Completely Unknown Input. 引用本文：具有完全未知输入的多智能体系统的基于多重变换矩阵的自适应投影涡流跟踪。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-07-29 DOI: 10.1109/TCYB.2026.3716178

Xiangyang Du, Jihong Shen, Shujuan Wang

{"title":"Retraction Notice: Multiple Transformation Matrix-Based Adaptive Projective Vortex Formation Tracking for Multiagent Systems With a Leader of Completely Unknown Input.","authors":"Xiangyang Du, Jihong Shen, Shujuan Wang","doi":"10.1109/TCYB.2026.3716178","DOIUrl":"10.1109/TCYB.2026.3716178","url":null,"abstract":"This article systematically studies projective vortex formation tracking (PVFT) of linear multiagent systems (MASs) on directed graphs through multiple transformation matrices, in which the input of leader and its upper bound information are not available to any follower. First, an innovative class of distributed adaptive observer is designed using the projection matrices to capture multiple desired virtual signals of the leader. Next, a novel kind of distributed PVFT protocol based on distributed observer, local observer and coordinates coupling matrices are proposed. Two different adaptive update mechanisms and nonlinear functions are introduced in the observer and controller to override the unknown input of the leader. Then, an algorithm is given, and the protocol under the algorithm is proved from three processes of estimation, aggregation, and rotation to enable linear systems to achieve PVFT. Finally, the effects of parameters on aggregation and rotation are analyzed systematically, and several simulation examples are given to illustrate the reliability of the results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148619855","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

Local Stabilization for Discrete-Time Fuzzy System With Guaranteed Resilience via Structural Relaxation 基于结构松弛的保证弹性离散模糊系统的局部镇定。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-07-01 Epub Date: 2026-02-18 DOI: 10.1109/TCYB.2026.3662010

KyungSoo Kim;Seongrok Moon;Hye Jin Lee;PooGyeon Park

{"title":"Local Stabilization for Discrete-Time Fuzzy System With Guaranteed Resilience via Structural Relaxation","authors":"KyungSoo Kim;Seongrok Moon;Hye Jin Lee;PooGyeon Park","doi":"10.1109/TCYB.2026.3662010","DOIUrl":"10.1109/TCYB.2026.3662010","url":null,"abstract":"This article aims to investigate relaxed local stabilization for discrete-time Takagi–Sugeno fuzzy systems with structural relaxation under guaranteed resilience. To mitigate the conservatism and computational burden associated with conventional multiple summation-type approaches for exploiting high-degree membership information, a novel Lyapunov function and nonparallel distributed compensation (non-PDC) control law are developed within an augmented membership-quadratic framework, which relaxes the symmetry constraints on the intertemporal cross terms. To overcome the limitations of existing resilient stabilization methods that rely heavily on a user-defined hyperparameter, a matrix-type threshold condition is introduced, enhancing both practicality and numerical efficiency. Based on orthogonal complements, new structural relaxation lemmas within the membership-quadratic framework are proposed for guaranteeing resilient stabilization. Finally, the effectiveness and reduced conservatism of the proposed method are validated through benchmark examples, demonstrating its computational efficiency and improved performance compared to existing approaches.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 7","pages":"3994-4004"},"PeriodicalIF":11.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146219701","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

Anti-Unwinding Time-Varying Sliding Mode Control With Arbitrary Convergence Time for Rigid Spacecraft 具有任意收敛时间的刚性航天器抗解卷时变滑模控制。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-07-01 Epub Date: 2026-02-18 DOI: 10.1109/TCYB.2026.3661967

Yu-Tian Xu;Youmin Gong;Ai-Guo Wu;Qing-Hua Zhu

引用次数: 0

Zentropy-Enhanced Multigranularity Knowledge Modeling for Robust Feature Selection 基于zentropy增强的多粒度知识建模鲁棒特征选择。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-07-01 Epub Date: 2026-02-26 DOI: 10.1109/TCYB.2026.3665802

Kehua Yuan;Duoqian Miao;Witold Pedrycz;Yiyu Yao

{"title":"Zentropy-Enhanced Multigranularity Knowledge Modeling for Robust Feature Selection","authors":"Kehua Yuan;Duoqian Miao;Witold Pedrycz;Yiyu Yao","doi":"10.1109/TCYB.2026.3665802","DOIUrl":"10.1109/TCYB.2026.3665802","url":null,"abstract":"Multigranularity knowledge modeling is an influential study for information processing and knowledge discovery in artificial intelligence (AI). A central research focus is the multigranularity representation and learning of knowledge structures. Among them, fuzzy rough sets (FRSs) have emerged as a representative method for characterizing uncertain knowledge. However, the existing FRS studies still exhibit two limitations: low robustness in knowledge acquisition and incomplete characterization of uncertainty. Hence, this article proposes a zentropy-enhanced multigranularity knowledge modeling framework for robust feature selection (ZeMG-FS). Specifically, we design a fast and adaptive multigranularity information granulation mechanism based on generalized granular-ball generation to effectively capture data distributions embedded in complex data. Then, the fuzzy rough approximation method is incorporated into the representation of multigranularity knowledge. Furthermore, we analyze the fundamental relationships and structures of the multigranularity knowledge model to introduce a novel multilevel zentropy. Unlike existing entropy measures, the primary consideration of the proposed zentropy is to match and enhance the performance of the proposed model. Finally, we design two feature evaluation criteria grounded in the model and apply them to feature selection. Extensive experiments demonstrate that our proposed methods achieve superior robustness and effectiveness compared with state-of-the-art approaches.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"56 7","pages":"4096-4107"},"PeriodicalIF":11.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147305265","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