IEEE Transactions on Cybernetics最新文献_第7页

Over the Edge of Chaos? Excess Complexity as a Roadblock to Artificial General Intelligence 越过混乱的边缘？过度复杂性是通用人工智能的障碍

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3609349

Teo Susnjak, Timothy R. McIntosh, Andre L. C. Barczak, Napoleon H. Reyes, Tong Liu, Paul Watters, Malka N. Halgamuge

引用次数: 0

Federated Learning Adaptive Dynamic Programming for Massive Multiagent Mean-Field Games-Based Optimal Consensus 基于平均场博弈的大规模多智能体最优共识联邦学习自适应动态规划

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3606528

Mingduo Lin, Guoling Yuan, Derong Liu

引用次数: 0

Data-Driven Control for Local Stabilization of Neural Networks Subject to Input Saturation: A Memory-Type Event-Triggered Method 输入饱和神经网络局部镇定的数据驱动控制：一种记忆型事件触发方法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3608051

Yanyan Ni, Xia Huang, Zhen Wang, Hao Shen

引用次数: 0

Solvability and Normalization of General Singular Boolean Networks via Admissible and Normal Initial State Sets. 广义奇异布尔网络的可解性与正则初始状态集。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3608175

Yifeng Li,Jun-E Feng,Yongduan Song

引用次数: 0

Multipeeling of Homogeneous Stationarity and Heterogeneous Nonstationarity With Differentiated Learning for Process Monitoring. 基于差异化学习的同质平稳性和异质非平稳性多重剥离过程监控。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3603684

Jianbo Yu,Jian Huang,Weimin Zhong,Qingchao Jiang,Xuefeng Yan,Xiaofeng Yang

{"title":"Multipeeling of Homogeneous Stationarity and Heterogeneous Nonstationarity With Differentiated Learning for Process Monitoring.","authors":"Jianbo Yu,Jian Huang,Weimin Zhong,Qingchao Jiang,Xuefeng Yan,Xiaofeng Yang","doi":"10.1109/tcyb.2025.3603684","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3603684","url":null,"abstract":"Nonstationarity in industrial processes, guided by factors, such as equipment aging and changing upstream load demands, inherently exhibits heterogeneous characteristics. This complex overlay of homogeneous stationarity poses great difficulty in process monitoring and analysis. Therefore, this study presents a new model (Hs-Hn) that peels the homogeneous and heterogeneous nonstationarity, which has four components: a differentiated learning network (DL-Net), a peeling network (Pe-Net), an adaptive reweighting network (AR-Net), and a global decoder network. DL-Net obtains the differentiated representation by leveraging a new differentiated learning approach to unique inputs, which is based on the cognitive understanding and derivation of functional specialization and content learning during network training. The aim is to maximize functional diversity and minimize content overlap. Furthermore, Pe-Net extracts the stationarity and nonstationarity (S-N) components from each differentiated scale, formulated as an encoder-decoder-encoder architecture with an integrated identity subtraction skip connection. A min-max S-N constraint regulates the peeling process and controls the extracted content. AR-Net additionally refines homogeneous stationarity across each scale and reweights the individual components to adaptively adjust their contributions. Last, reweighted components are fused and input into the global decoder to facilitate unsupervised learning. Experimental results on three processes demonstrate the effectiveness of Hs-Hn.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"28 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145117068","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

Prespecified-Performance-Driven Triggering Consensus of Nonlinear Multiagent Systems With Unknown Actuator Faults 具有未知执行器故障的非线性多智能体系统的预定性能驱动触发一致性

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3607803

Xiaoan Wang, Xiaobing Nie, Jinde Cao, Liang Hua

引用次数: 0

New Results on Memory Sampled-Data Control Design for IT2 Fuzzy Singular Systems With External Disturbance. 具有外部干扰的IT2模糊奇异系统的记忆采样数据控制设计新结果。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-22 DOI: 10.1109/tcyb.2025.3607281

T Saravanakumar,A Stephen,Xiaoshan Bai,Quanxin Zhu,Tingwen Huang

引用次数: 0

Variable Threshold-Oriented Event-Triggered Cluster Consensus for Groups of Multiagent Systems. 面向可变阈值的多智能体系统的事件触发聚类一致性。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-19 DOI: 10.1109/tcyb.2025.3607967

Mahrukh,Muhammad Rehan,Abdul Basit,Ijaz Ahmed,Choon Ki Ahn,Mohammed Chadli

{"title":"Variable Threshold-Oriented Event-Triggered Cluster Consensus for Groups of Multiagent Systems.","authors":" Mahrukh,Muhammad Rehan,Abdul Basit,Ijaz Ahmed,Choon Ki Ahn,Mohammed Chadli","doi":"10.1109/tcyb.2025.3607967","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3607967","url":null,"abstract":"This article investigates the leader-following cluster consensus for generic linear heterogeneous multiagent systems (MASs). Unlike the existing research, a novel event-triggered (ET) control mechanism is designed and developed on the transmission side of the agents, over directed communication topologies, to reduce communication load. For this purpose, a variable threshold function as the fully distributed ET condition (ETC) is suggested, which provides a smooth transition and considers both maximum and minimum threshold levels for triggering. A relative-state feedback-based cluster consensus control protocol is designed by considering the cooperative and competitive interaction behavior of agents. Then, the convergence analysis is performed by utilizing the Lyapunov method. This work is then further extended for the ET observer-based output feedback cluster consensus problem. The proposed ETC naturally eliminates the Zeno behavior for each agent. In contrast to existing methods, a variable threshold-based ET scheme, a cooperation-competition network, and an elimination of Zeno behavior for both state-based and output-based methods have been considered for the leader-following cluster consensus. Finally, illustrative examples are used to validate the theoretical results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"18 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089790","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-Constrained Prescribed Performance Control of MIMO Nonlinear Systems With a Priori Unknown References 具有先验未知参考的MIMO非线性系统的输出约束规定性能控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-19 DOI: 10.1109/tcyb.2025.3605862

Jin-Xi Zhang, Jia Di, Witold Pedrycz, Zhongmei Li

引用次数: 0

Locally Active Memristor Cooperatively-Controlled Fast-Slow Dynamics in Morris–Lecar Neuron Model and FPGA Implementation 局部有源忆阻器协同控制的快慢动态Morris-Lecar神经元模型及FPGA实现

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-19 DOI: 10.1109/tcyb.2025.3606973

Xihong Yu, Lilian Huang, Jun Mou, Yan Yang, Qiang Guo

引用次数: 0