IEEE Transactions on Fuzzy Systems最新文献_第6页

Evolving Gaussian Systems as a Framework for Federated Regression Problems 演化高斯系统作为联邦回归问题的框架

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-22 DOI: 10.1109/TFUZZ.2025.3601900

Miha Ožbot;Paulo Vitor Campos Souza;Igor Škrjanc

{"title":"Evolving Gaussian Systems as a Framework for Federated Regression Problems","authors":"Miha Ožbot;Paulo Vitor Campos Souza;Igor Škrjanc","doi":"10.1109/TFUZZ.2025.3601900","DOIUrl":"10.1109/TFUZZ.2025.3601900","url":null,"abstract":"In this article, we present a novel federated learning framework to multivariate regression problems, termed evolving Gaussian federated regression (eGauss+<inline-formula><tex-math>$_{text{FR}}$</tex-math></inline-formula>). The need for a federated approach is due to the increasing problem of distributed acquisition of the data and protection for the rights of distributing these data. Regression problems are usually nonlinear and, therefore, strongly connected to the clustering to divide the data space into smaller subspaces where a linear approximation could be applied. Here, we are faced with the main drawback of traditional clustering methods, where a predefined number of clusters are needed. In federated learning problems, where the data are commonly nonidentically distributed between different sources or clients, this represents a significant challenge. This problem can be overcome by introducing an evolving approach, which adds and removes the clusters on-the-fly. The idea in our approach is to use the incremental c-regression or c-varieties clustering methods to define the clusters, which lie close to the lines and describe them with the centers and the covariance matrices. The clustering is done for each data source or client. Due to the restriction and protection of data sharing, only the centers and the covariance matrices of all clients are then transmitted to main server and merged together, which is here done in a way as proposed in eGauss+ method. From merged clusters the auxiliary points are generated, which than serve to approximate the function by using classical fuzzy models. Our proposed method was demonstrated on simple synthetic data, while synthetic and real-world datasets were used to test time complexity and scalability with the number of clients. The results demonstrate the benefits of evolving federated method, which results in high-quality approximation of the function and can be easily extended to high-dimensional problems.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3736-3746"},"PeriodicalIF":11.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evidential Reasoning With Divisive Hierarchical Clustering for Multisource Information Fusion 基于分裂层次聚类的多源信息融合证据推理

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-21 DOI: 10.1109/TFUZZ.2025.3601509

Kezhu Zuo;Xinde Li;Le Yu;Kaixuan Wu;Siyuan Li;Yilin Dong;Zhijun Li

{"title":"Evidential Reasoning With Divisive Hierarchical Clustering for Multisource Information Fusion","authors":"Kezhu Zuo;Xinde Li;Le Yu;Kaixuan Wu;Siyuan Li;Yilin Dong;Zhijun Li","doi":"10.1109/TFUZZ.2025.3601509","DOIUrl":"10.1109/TFUZZ.2025.3601509","url":null,"abstract":"Dempster–Shafer (DS) evidence theory provides a powerful framework for modeling uncertainty, reasoning, and combining information from multiple sources. However, it may yield counterintuitive results when handling conflicting evidence, thereby affecting decision reliability and limiting practical applications. To address this issue, this work proposes a novel evidential reasoning rule with divisive hierarchical clustering (ER-DHC), consisting of two main modules: evidence clustering and cluster fusion. At first, a new divisive hierarchical algorithm is introduced for evidence clustering, comprising coarse-grained and fine-grained division. In the coarse-grained stage, evidence with different decision preferences is grouped into separate clusters, thus preventing high intracluster conflicts and laying a solid foundation for evidence clustering. The fine-grained division adaptively refines cluster structures using an inflection point detection method, thereby enhancing clustering quality. On this basis, a new cluster fusion strategy is developed, involving intracluster fusion via classical Dempster’s rule and intercluster fusion using a fuzzy preference relation-based weighted approach. This fusion strategy can degenerate into classical DS fusion and weighted fusion, while also introducing a new clustering fusion perspective, offering better flexibility. Finally, the proposed ER-DHC method is applied to the multisource information fusion system, with experimental results demonstrating improved performance of target classification.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3707-3721"},"PeriodicalIF":11.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898493","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

Prescribed-Time Cooperative Control of Multilateral Teleoperation Systems: A Novel Composite Fuzzy Learning-Based Approach 多边遥操作系统的规定时间协同控制：一种基于复合模糊学习的新方法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-20 DOI: 10.1109/TFUZZ.2025.3596302

Huixin Jiang;Yana Yang;Xinru Feng;Changchun Hua;Junpeng Li

{"title":"Prescribed-Time Cooperative Control of Multilateral Teleoperation Systems: A Novel Composite Fuzzy Learning-Based Approach","authors":"Huixin Jiang;Yana Yang;Xinru Feng;Changchun Hua;Junpeng Li","doi":"10.1109/TFUZZ.2025.3596302","DOIUrl":"10.1109/TFUZZ.2025.3596302","url":null,"abstract":"In this article, a novel prescribed-time composite learning-enhanced fuzzy (PrTCLF) cooperative control approach is proposed for the multiple-leadermultiple-follower (MLMF) teleoperation systems in the presence of system model uncertainties and external interferences. First, compared with traditional MLMF systems, a unifying virtual leader–follower teleoperation control framework, notably applicative for more general situations where the number of leaderand follower robots is not the same, is constructed by introducing scheduled control authority for different operators, especially in cooperative control mode. A significant feature of this article is that the first result of a novel time-dependent function integrated PrTCLF learning law rendering all the synchronization errors of the uncertain MLMF teleoperation system to zero is creatively derived, by which the effect of the traditional fuzzy learning error on the precision of system convergence is essentially solved. Meanwhile, in order to ensure the high efficiency and robustness of cooperative work, a new class of nonsingular prescribed-time terminal sliding mode surface is designed without any switching behavior. Besides, the sufficient conditions for maintaining the prescribed-time stability of the MLMF teleoperation system are provided through systematic Lyapunov stability analysis. Finally, the effectiveness of the control structure and algorithm is verified by a large number of simulation and experimental results.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3695-3706"},"PeriodicalIF":11.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898545","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

CS3W-GBG: A Cost-Sensitive Three-Way Granular-Ball Generation Method CS3W-GBG：一种成本敏感的三向颗粒球生成方法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-20 DOI: 10.1109/tfuzz.2025.3596066

Jie Yang, Fan Zhao, Guoyin Wang, Witold Pedrycz, Shuyin Xia, Yanmin Liu, Qinghua Zhang

引用次数: 0

Fully Interpretable Gaussian Centralized TSK Fuzzy Classifier from a Probabilistic Perspective: Concepts, Output-Stability-Based Learning, and Ensemble 从概率角度看完全可解释的高斯集中式TSK模糊分类器：概念、基于输出稳定性的学习和集成

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-20 DOI: 10.1109/tfuzz.2025.3600911

Yuchen Li, Fu-lai Chung, Yusuke Nojima, Shitong Wang

引用次数: 0

Global Fuzzy Tracking Control for Uncertain High-Order Odd-Rational-Power Systems With Sensor Faults 具有传感器故障的不确定高阶奇有理电力系统全局模糊跟踪控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-19 DOI: 10.1109/TFUZZ.2025.3600264

Bosong Wei;Xiaokui Yue;Zongcheng Liu;Xiucai Huang;Zhaohui Dang;Maolong Lv

引用次数: 0

An Advanced Type-2 Fuzzy Inference for Rapid Convergence in Adaptive Communication Control 一种用于自适应通信控制快速收敛的先进2型模糊推理

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-13 DOI: 10.1109/tfuzz.2025.3598437

Yang Lu, Yonggang Liang, Ziyi Bian, Yan Zheng, Wei Xiang

引用次数: 0

A Dynamic Interval Type-2 Fuzzy Stochastic Configuration Network for Nonlinear System Modeling 非线性系统建模的动态区间2型模糊随机组态网络

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-11 DOI: 10.1109/tfuzz.2025.3596702

Jian Sun, Kexin Ma, Yang Feng

引用次数: 0

Synchronization Control for Discrete-Time Fuzzy Complex Networks: A Novel State-Dependent Intermittent Memory-Event-Triggered Scheme 离散时间模糊复杂网络的同步控制：一种新的状态相关间歇记忆事件触发方案

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-06 DOI: 10.1109/tfuzz.2025.3570996

Zhihong Liang, Huaguang Zhang, Juan Zhang, Qiongwen Zhang

引用次数: 0

Guest Editorial: Special Issue on Fuzzy Intelligence for Flexible Electronics and Systems 专题：柔性电子与系统的模糊智能

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-06 DOI: 10.1109/tfuzz.2025.3590805

Haisheng Xia, Jonathan M. Garibaldi, Guanglin Li, Zhijun Li

引用次数: 0