IEEE Transactions on Fuzzy Systems最新文献_第10页

Finite-Time Resilient Filtering for Discrete-Time T-S Fuzzy Networked Control Systems with Switching Communication Channels under Cyber Attacks 网络攻击下具有切换通信信道的离散时间 T-S 模糊网络控制系统的有限时间弹性过滤

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-07 DOI: 10.1109/tfuzz.2024.3494016

Ramalingam Sakthivel, Oh-Min Kwon, Myeong Jin Park, Rathinasamy Sakthivel

引用次数: 0

Complete synchronization of discrete-time fractional-order T-S fuzzy complex-valued neural networks with time delays and uncertainties 具有时间延迟和不确定性的离散时间分数阶 T-S 模糊复值神经网络的完全同步化

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-05 DOI: 10.1109/tfuzz.2024.3492011

Rong Chen, Hong-Li Li, Heng Liu, Haijun Jiang, Jinde Cao

引用次数: 0

Exploring the Potential of Fuzzy Sets in Cyborg Enhancement: A Comprehensive Review 探索模糊集在半机械增强中的潜力：全面回顾

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-05 DOI: 10.1109/tfuzz.2024.3491733

Hang Su, Salih Ertug Ovur, Zhaoyang Xu, Samer Alfayad

引用次数: 0

Hierarchical Rule-Base Reduction Fuzzy Control for Path Tracking Variable Linear Speed Differential Steer Vehicles 用于路径跟踪变线速差速转向车的分层规则库还原模糊控制技术

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-05 DOI: 10.1109/tfuzz.2024.3491059

Samuel R. Dekhterman, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu

引用次数: 0

Fuzzy Secure Formation Control for NMASs: A Prescribed Performance Scaling Framework NMAS 的模糊安全编队控制：规定性能缩放框架

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-04 DOI: 10.1109/TFUZZ.2024.3490607

Dianbiao Dong;Jiahe Huo;Tao Xu;Dengxiu Yu;Zhen Wang

引用次数: 0

Fuzzy Min-Cut With Soft Balancing Effects 具有软平衡效应的模糊最小切割

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-04 DOI: 10.1109/TFUZZ.2024.3491300

Huimin Chen;Runxin Zhang;Rong Wang;Feiping Nie

{"title":"Fuzzy Min-Cut With Soft Balancing Effects","authors":"Huimin Chen;Runxin Zhang;Rong Wang;Feiping Nie","doi":"10.1109/TFUZZ.2024.3491300","DOIUrl":"10.1109/TFUZZ.2024.3491300","url":null,"abstract":"The clustering algorithm has always been a hot spot in machine learning, which has made great progress and been widely used in different scenarios. Due to the characteristics and requirements of some application scenarios, the branch of the balanced clustering algorithm has been developed. The ideal of these algorithms is to obtain clusters containing approximately the same number of samples. However, when there are data points distributed at the boundary of different clusters, resulting in different probabilities of their belonging, hard-partitioned balanced clustering may not be able to handle these boundary data well, thus limiting their performance. Motivated by this, we propose a Fuzzy Min-Cut with Soft Balancing Effects (FCBE) method in this article. Specifically, the FCBE model utilizes fuzzy constraints to simultaneously enhance the ability of the balanced algorithm to capture boundary data members and the advantage of directly obtaining the partitioning results of graph-cut problem without postprocessing. In addition, a sparse regularization is introduced to avoid trivial solutions and maintain the separability of the relationship matrix. Furthermore, the proposed FCBE method can be viewed as a flexibly adjustable generalization pattern that not only has clear interpretability but also can become special cases with clear physical meanings under different parameter values. The feasibility of FCBE has been verified on real datasets.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 2","pages":"767-778"},"PeriodicalIF":10.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580371","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

A ZNN-Based Solver With Adaptive Input Range Fuzzy Logic System for Time-Varying Algebraic Riccati Equation 基于 ZNN 的时变代数 Riccati 方程自适应输入范围模糊逻辑系统求解器

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-04 DOI: 10.1109/TFUZZ.2024.3491194

Lin Xiao;Dan Wang;Qiuyue Zuo;Xiangru Yan;Hang Cai

{"title":"A ZNN-Based Solver With Adaptive Input Range Fuzzy Logic System for Time-Varying Algebraic Riccati Equation","authors":"Lin Xiao;Dan Wang;Qiuyue Zuo;Xiangru Yan;Hang Cai","doi":"10.1109/TFUZZ.2024.3491194","DOIUrl":"10.1109/TFUZZ.2024.3491194","url":null,"abstract":"Time-varying algebraic Riccati equations (TAREs) indeed play a crucial role in science and engineering with widespread applications. This research combines the advantages of zeroing neural network (ZNN) in handling time-varying problems with the flexibility of fuzzy logic system (FLS), proposing a ZNN-based solver for solving the TARE. One of the innovations of this article is the presentation of an adaptive input range fuzzy logic system (AFLS) with portability and adaptability, offering a novel approach for determining the input range of the FLS. The method effectively resolves the current dilemma of relying on a specific problem and model for determining the FLS input range. In addition, to enhance convergence speed and achieve predefined-time convergence of the fuzzy predefined-time robust zeroing neural network (FPRZNN) model, we introduce a novel segmental predefined-time robust activation function (SPRAF). Furthermore, three key theorems are proposed to prove the stability, convergence, and robustness of the FPRZNN model. Finally, the numerical simulations showcase the superior convergence and robustness of the FPRZNN model compared to other existing ZNN models.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 2","pages":"757-766"},"PeriodicalIF":10.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580411","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

Dual Anchor Graph Fuzzy Clustering for Multiview Data 多视角数据的双锚图模糊聚类

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-11-01 DOI: 10.1109/TFUZZ.2024.3489025

Wei Zhang;Xiuyu Huang;Andong Li;Te Zhang;Weiping Ding;Zhaohong Deng;Shitong Wang

{"title":"Dual Anchor Graph Fuzzy Clustering for Multiview Data","authors":"Wei Zhang;Xiuyu Huang;Andong Li;Te Zhang;Weiping Ding;Zhaohong Deng;Shitong Wang","doi":"10.1109/TFUZZ.2024.3489025","DOIUrl":"10.1109/TFUZZ.2024.3489025","url":null,"abstract":"Multiview anchor graph clustering has been a prominent research area in recent years, leading to the development of several effective and efficient methods. However, three challenges are faced by current multiview anchor graph clustering methods. First, real-world data often exhibit uncertainty and poor discriminability, leading to suboptimal anchor graphs when directly extracted from the original data. Second, most existing methods assume the presence of common information between views and primarily explore it for clustering, thus neglecting view-specific information. Third, further exploration and exploitation of the learned anchor graph to enhance clustering performance remains an open research question. To address these issues, a novel dual anchor graph fuzzy clustering method is proposed in this article. First, a novel matrix factorization-based dual anchor graph learning method is proposed to address the first two issues by extracting highly discriminative hidden representations for each view and subsequently deriving both common and specific anchor graphs from these hidden representations. Then, to address the third issue, a novel anchor graph fuzzy clustering method is developed with cooperative learning to exploit and utilize the common and specific anchor graphs fully. Meanwhile, a fuzzy membership structure preservation mechanism with dual anchor graphs is constructed to enhance clustering performance. Finally, negative Shannon entropy is further introduced to adaptively adjust the view weighing. Extensive experiments on several datasets demonstrate the effectiveness of the proposed method.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 2","pages":"730-744"},"PeriodicalIF":10.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563111","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

IEEE Transactions on Fuzzy Systems Publication Information IEEE Transactions on Fuzzy Systems 出版信息

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-31 DOI: 10.1109/TFUZZ.2024.3480373

引用次数: 0

Observer-Based Optimal Fuzzy Control for Networked IT-2 Fuzzy Systems Against Hybrid Cyber-Attacks 基于观测器的网络 IT-2 模糊系统最佳模糊控制，对抗混合网络攻击

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-31 DOI: 10.1109/TFUZZ.2024.3488678

Yang Li;Ju H. Park;Yang Gu;Yong He

{"title":"Observer-Based Optimal Fuzzy Control for Networked IT-2 Fuzzy Systems Against Hybrid Cyber-Attacks","authors":"Yang Li;Ju H. Park;Yang Gu;Yong He","doi":"10.1109/TFUZZ.2024.3488678","DOIUrl":"10.1109/TFUZZ.2024.3488678","url":null,"abstract":"This article investigates observer-based optimal fuzzy control for networked interval type-2 (IT-2) fuzzy systems against hybrid cyber-attacks, including aperiodic denial-of-service and deception attacks. First, a hybrid cyber-attack model is constructed after denoting a nonlinear system with parameter uncertainties and external disturbances via an IT-2 fuzzy model. Then, the switched fuzzy observer is derived to estimate the unmeasured states of the system. Second, by introducing the newly designed algorithm, a novel optimal adaptive fuzzy event-triggered strategy is proposed. This mechanism effectively reduces the communication burden and enhances flexibility in handling hybrid cyber-attacks. Third, by employing a new piecewise Lyapunov–Krasovskii functional that incorporates membership functions (MFs), a fuzzy controller with imperfect MFs is designed, where both the MFs and their derivatives are considered. Eventually, sufficient conditions are developed to ensure that the networked IT-2 system is mean square exponentially stable with <inline-formula><tex-math>$ H_infty$</tex-math></inline-formula> performance. Finally, the merits and applicability of the proposed approaches are exemplified by a practical example.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 2","pages":"704-716"},"PeriodicalIF":10.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561892","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