IEEE Transactions on Cybernetics最新文献_第5页

Adaptive Fuzzy Control of Networked Hidden Stochastic Switching Power Systems Under Cyber Attacks 网络攻击下网络隐式随机开关电源系统的自适应模糊控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-19 DOI: 10.1109/tcyb.2025.3567360

Wenhai Qi, Mingxuan Sha, Guangdeng Zong, Shun-Feng Su, Jinde Cao, Ruey-Huei Yeh, Lulu Jiang

引用次数: 0

Piecewise Constant Tuning Gain-Based Singularity-Free MRAC With Application to Aircraft Control Systems 基于分段恒整定增益的无奇异MRAC在飞机控制系统中的应用

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-19 DOI: 10.1109/tcyb.2025.3567177

Zhipeng Zhang, Yanjun Zhang, Jian Sun

引用次数: 0

Exploring Fine-Grained Visual-Text Feature Alignment With Prompt Tuning for Domain-Adaptive Object Detection 探索细粒度的视觉文本特征对齐与提示调整领域自适应对象检测

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-19 DOI: 10.1109/tcyb.2025.3567126

Zhitao Wen, Jinhai Liu, Huaguang Zhang, Fengyuan Zuo

引用次数: 0

Decentralized Adaptive Secure Control of Nonlinear NCSs Under Hybrid Attacks via Event-Triggering 基于事件触发的混合攻击下非线性ncs分散自适应安全控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-14 DOI: 10.1109/tcyb.2025.3567457

Weiwei Sun, Lusong Ding, Yongshu Li, Dehai Yu

引用次数: 0

Sliding-Mode Control for Sojourn-Probability-Based Switching Systems With Cyber-Attacks 网络攻击下基于逗留概率的交换系统滑模控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-13 DOI: 10.1109/tcyb.2025.3565806

Jun Cheng, Qiongwen Zhang, Huaicheng Yan, Dan Zhang, Ju H. Park

引用次数: 0

A Data-Driven Predictive Control Scheme for Nonlinear Discrete-Time Systems 非线性离散系统的数据驱动预测控制方案

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-13 DOI: 10.1109/tcyb.2025.3565580

Juanping Zhu, Qiuyan Wei, Xian Yu, Zhongsheng Hou

引用次数: 0

Self-Adjustable and Flexible Performance-Based Event-Triggered Asymptotic Tracking Control of Nonlinear Systems With Unknown Control Directions 未知控制方向非线性系统基于性能的自调柔性事件触发渐近跟踪控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-09 DOI: 10.1109/tcyb.2025.3563585

Haibin Sun, Xiangling Kong, Jun Yang, Linlin Hou, Dong Yang

引用次数: 0

Discrete Memristive Conservative Chaotic Map: Dynamics, Hardware Implementation, and Application in Secure Communication. 离散记忆保守混沌映射：动力学、硬件实现和在安全通信中的应用。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-08 DOI: 10.1109/tcyb.2025.3565333

Quanli Deng,Chunhua Wang,Yichuang Sun,Gang Yang

{"title":"Discrete Memristive Conservative Chaotic Map: Dynamics, Hardware Implementation, and Application in Secure Communication.","authors":"Quanli Deng,Chunhua Wang,Yichuang Sun,Gang Yang","doi":"10.1109/tcyb.2025.3565333","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3565333","url":null,"abstract":"The randomness of chaotic systems are crucial for their application in secure communication. Conservative systems exhibit enhanced ergodicity and randomness in comparison to dissipative chaotic systems. However, the memristor-based conservative chaotic maps remain unreported. This article presents a study of volume-preserving chaotic maps based on discrete memristor (DM). We propose and analyze a generic conservative map that incorporates DM. The conservative characteristics of the proposed iterative map are confirmed through the determinant of its Jacobian matrix. Furthermore, four distinct DM models are introduced and their memristive characteristics are verified through numerical simulations of hysteresis loops. To investigate the dynamical properties of the discrete memristive conservative map (DMCM), we incorporate the proposed DM models into the generic conservative map model using numerical methods, including phase portraits, Lyapunov exponents, and bifurcation diagrams. Additionally, the hardware implementation of the DMCM on an FPGA platform demonstrates the reliability of the model. Finally, secure communication experiments based on the DMCM show that it outperforms some classical dissipative chaotic maps in terms of bit error rate performance.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"120 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926524","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 VFO Control Design for Nonholonomic Mobile Robots With Constrained Control Inputs. 控制输入受限的非完整移动机器人定时VFO控制设计。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-08 DOI: 10.1109/tcyb.2025.3564036

Rafal Mateusz Sobanski,Maciej Marcin Michalek,Michael Defoort

引用次数: 0

Robust Multiobjective Competitive Swarm Optimization Based on Evolutionary Trend Prediction. 基于进化趋势预测的鲁棒多目标竞争群优化。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-08 DOI: 10.1109/tcyb.2025.3565010

Honggui Han,Hao Zhou,Yanting Huang,Ying Hou

{"title":"Robust Multiobjective Competitive Swarm Optimization Based on Evolutionary Trend Prediction.","authors":"Honggui Han,Hao Zhou,Yanting Huang,Ying Hou","doi":"10.1109/tcyb.2025.3565010","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3565010","url":null,"abstract":"The competitive swarm optimizer (CSO) has been widely used for addressing multiobjective optimization problems owing to its diverse learning approach. However, the evolutionary process uncertainty within the algorithm weakens the optimization reliability. To deal with this concern, a robust multiobjective CSO with a predictive indicator (RMOCSO-PI), is proposed. This approach can reduce aimless and inefficient searches caused by the uncertainty to enhance algorithmic robustness. First, a predictive indicator is established based on the autoregressive model, which utilizes historical swarm distribution data to predict the evolutionary trends. Then, the particles are classified into winners and losers by evaluating their evolutionary potential, whose evolution would be guided differentially. Second, a space fusion-based competitive mechanism is designed to generate precise evolution directions for loser particles. The space fusion-based adaptive adjustment method integrates the learning cost metric in decision space with the learning worth metric in objective space for proper learning weight settings. Third, a dynamic cooperative mechanism is presented to purposefully guide the diversity exploration of particles. By estimating evolutionary states, three cooperative patterns are dynamically assigned to particles for purposeful diversity exploration. To provide theoretical support for the validity and reliability of RMOCSO-PI, a convergence analysis is given. Furthermore, experimental results verify that RMOCSO-PI has more stable and excellent optimization performance.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926526","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