IEEE Transactions on Fuzzy Systems最新文献_第5页

Partial Multilabel Learning via Dynamic Fuzzy Aggregations of Multigranularity Features 基于多粒度特征动态模糊聚合的部分多标签学习

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-03 DOI: 10.1109/TFUZZ.2025.3584340

Anhui Tan;Jianhang Xu;Wei-Zhi Wu;Weiping Ding;Jiye Liang

{"title":"Partial Multilabel Learning via Dynamic Fuzzy Aggregations of Multigranularity Features","authors":"Anhui Tan;Jianhang Xu;Wei-Zhi Wu;Weiping Ding;Jiye Liang","doi":"10.1109/TFUZZ.2025.3584340","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3584340","url":null,"abstract":"Partial multilabel learning is a pivotal area in machine learning that tackles scenarios where training instances are annotated with a set of candidate labels, only a subset of which is relevant. Existing approaches typically rely on global-level feature learning or noise disambiguation; however, they often struggle to effectively capture the multigranularity relationships inherent in feature and label spaces, and tend to overlook critical intrafeature information essential for accurate label discrimination. To address these limitations, we propose a novel partial multilabel learning framework based on a dynamic coarse-to-fine granularity feature aggregation strategy, which hierarchically extracts feature representations across multiple levels of granularity and dynamically emphasizes label-relevant feature components. Specifically, the dynamic fine-granularity graph captures label-specific local information by modeling the fuzzy aggregations among fine-granularity feature components, while the dynamic coarse-granularity graph learns adaptive label representations by identifying feature-aware correlations of labels and suppressing noise. By jointly leveraging these two complementary granularity levels, the model effectively integrates multilevel semantic relationships and enhances the overall discriminative capacity of the learned features. Extensive experiments conducted on benchmark datasets under varying noise conditions demonstrate that the proposed method consistently outperforms state-of-the-art approaches in partial multilabel classification.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3156-3167"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934419","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

Sampling-Fuzzy-Dependent LKF for T-S Fuzzy Systems Under Sampled-Data Control 抽样数据控制下T-S模糊系统的抽样模糊相关LKF

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-02 DOI: 10.1109/TFUZZ.2025.3605340

Zhou-Zhou Liu;Li Jin;Yong He

引用次数: 0

Memory-Event-based Distributed T-S Fuzzy Security Control for A Class of Cyber-Physical Systems under Replay Attack 重放攻击下一类网络物理系统基于内存事件的分布式T-S模糊安全控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-02 DOI: 10.1109/tfuzz.2025.3605090

Yi Shui, Lu Dong, Ya Zhang, Changyin Sun

引用次数: 0

FHN: Fuzzy Hashing Network for Medical Image Retrieval 模糊哈希网络在医学图像检索中的应用

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-02 DOI: 10.1109/TFUZZ.2025.3595736

Weiping Ding;Linlin Zhou;Wei Zhang;Te Zhang;Zhaohong Deng;Yuanpeng Zhang;Guanjin Wang

{"title":"FHN: Fuzzy Hashing Network for Medical Image Retrieval","authors":"Weiping Ding;Linlin Zhou;Wei Zhang;Te Zhang;Zhaohong Deng;Yuanpeng Zhang;Guanjin Wang","doi":"10.1109/TFUZZ.2025.3595736","DOIUrl":"10.1109/TFUZZ.2025.3595736","url":null,"abstract":"The rapid advancement of medical imaging technologies has led to an exponential increase in medical image data, making efficient retrieval from large-scale datasets critical for improving diagnostic accuracy and speed. However, two key challenges hinder this process: first, the presence of uncertain and subtle lesions in medical images that are often difficult to discern, and second, class imbalance across different case types within medical image databases. These inherent challenges significantly degrade the performance of existing hashing algorithms. In recent years, methods based on the Takagi–Sugeno–Kang fuzzy system (TSK-FS) have shown promising performance in medical image modeling. Inspired by these advances, this article proposes a novel fuzzy hashing network (FHN) based on TSK-FS to enhance retrieval performance by effectively handling both uncertainty and data imbalance in medical imaging. The FHN first introduces a novel fuzzification mechanism that incorporates the concept of a self-attention mechanism to effectively capture the complex underlying features in medical images, thereby enhancing the data discriminability in fuzzy spaces. Meanwhile, a new consequent parameter learning mechanism is developed for defuzzification by introducing the Transformer network, which aims to improve the inference efficiency and generalization capability of the FHN. Based on these two mechanisms, FHN’s capability of analyzing and handling uncertain data is significantly enhanced. Furthermore, a novel hash center loss is designed to capture global relationships while emphasizing local structural information, thereby improving the handling of imbalanced data and significantly enhancing retrieval performance.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3770-3783"},"PeriodicalIF":11.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930765","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

SL-ANFIS-LSTM: A Structure Learnable Fuzzy Neural Network for Ultra-Short-Term PV Power Forecasting l - anfiss - lstm：用于超短期光伏发电预测的结构可学习模糊神经网络

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-01 DOI: 10.1109/tfuzz.2025.3604847

Zhao Su, Jun Shen, Qingguo Zhou, Binbin Yong

引用次数: 0

Distributed Fuzzy Adaptive Nash Equilibrium Control for Nonlinear MASs Under Unreliable Communication Networks 不可靠通信网络下非线性质量的分布式模糊自适应纳什平衡控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-01 DOI: 10.1109/TFUZZ.2025.3604444

Haodong Zhou;Yongming Li;Shaocheng Tong

{"title":"Distributed Fuzzy Adaptive Nash Equilibrium Control for Nonlinear MASs Under Unreliable Communication Networks","authors":"Haodong Zhou;Yongming Li;Shaocheng Tong","doi":"10.1109/TFUZZ.2025.3604444","DOIUrl":"10.1109/TFUZZ.2025.3604444","url":null,"abstract":"This article investigates the distributed fuzzy adaptive Nash equilibrium (NE) seeking problem in noncooperative games for nonlinear multiagent systems under unreliable communication networks. Since the considered unreliable communication networks are jointly strongly connected switching networks and suffer from time delays, agents cannot receive their neighboring agents’ actions or can only obtain delayed actions. To estimate the neighboring agents’ actions, a distributed NE seeking observer is developed. Then, based on the proposed distributed NE seeking observer and backstepping control technology, a distributed fuzzy adaptive control scheme is constructed by utilizing fuzzy logic systems and adopting integrable functions and bounded parameter estimation algorithms. It is proven that the observation error of the distributed NE seeking observer asymptotically converges to zero, and the developed fuzzy adaptive control scheme can ensure that the agents’ outputs asymptotically converge to the NE of the noncooperative game. Moreover, the nondifferentiable problem of virtual controllers is avoided. Finally, we apply the distributed fuzzy adaptive control scheme to marine surface vehicles, and the simulation and comparison results confirm its effectiveness.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3760-3769"},"PeriodicalIF":11.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144928063","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

Fuzzy Spatiotemporal Knowledge Graph Queries via Global and Local Uncertain Dynamic Subgraph Reasoning Using Embedding 基于嵌入的全局和局部不确定动态子图推理的模糊时空知识图查询

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-09-01 DOI: 10.1109/tfuzz.2025.3604071

Hao Ji, Li Yan, Zongmin Ma

引用次数: 0

Multi-View Fuzzy Clustering for Multi-Layer and Multi-Attribute Graphs 多层多属性图的多视图模糊聚类

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-28 DOI: 10.1109/tfuzz.2025.3597439

Xianghui Hu, Jie Chen, Guorui Chen, Yiming Tang, Witold Pedrycz, Yichuan Jiang

引用次数: 0

Game-Theoretic Optimal Fixed-Time Adaptive Control for Fuzzy Mechanical Systems With Prescribed Performance 具有规定性能的模糊机械系统的博弈论最优定时自适应控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-25 DOI: 10.1109/TFUZZ.2025.3602690

Chao Ma;Kang Huang;Jinchuan Zheng;Hao Sun;Demeng Qian;Ke Shao

{"title":"Game-Theoretic Optimal Fixed-Time Adaptive Control for Fuzzy Mechanical Systems With Prescribed Performance","authors":"Chao Ma;Kang Huang;Jinchuan Zheng;Hao Sun;Demeng Qian;Ke Shao","doi":"10.1109/TFUZZ.2025.3602690","DOIUrl":"10.1109/TFUZZ.2025.3602690","url":null,"abstract":"An optimal prescribed performance control with fixed-time for fuzzy mechanical systems is explored. Specifically, a novel bounded performance function is proposed, which preassigns the convergence time, transient convergence trend (dynamic, variable and gentle initial stage) and steady-state tracking accuracy. More possibilities for transient performance of fuzzy systems are developed. Then, fuzzy set theory is introduced to describe system uncertainties. A fuzzy mechanical system with prescribed performance is constructed in the homeomorphism mapping space. An adaptive control method with finite-time stability is proposed. Thus, the preassigned performance is met through a two-layer collaborative convergence characteristic, rather than relying solely on performance constraint. High-order adaptive laws reduce control costs and avoid overcompensation. Control design is always deterministic rather than based on fuzzy rules. A more effective way is reflected in fuzzy-based optimization. Based on fuzzy sets to measure uncertainty, a cooperative game optimization strategy is designed to obtain the optimal decision for multiple objectives and control parameters. The best combination of performance and cost is solved. The effectiveness of the proposed method is verified via the steer-by-wire system.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3747-3759"},"PeriodicalIF":11.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900422","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

Asynchronous Observer Design for Fuzzy Control of Nonlinear Semi-Markov Jump Singularly Perturbed Systems 非线性半马尔可夫跳变奇摄动系统模糊控制的异步观测器设计

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-08-22 DOI: 10.1109/TFUZZ.2025.3601891

Ziwei Zhang;Shengyuan Xu;Baoyong Zhang;Qian Ma;Deming Yuan

{"title":"Asynchronous Observer Design for Fuzzy Control of Nonlinear Semi-Markov Jump Singularly Perturbed Systems","authors":"Ziwei Zhang;Shengyuan Xu;Baoyong Zhang;Qian Ma;Deming Yuan","doi":"10.1109/TFUZZ.2025.3601891","DOIUrl":"10.1109/TFUZZ.2025.3601891","url":null,"abstract":"This article provides a novel framework for the concurrent development of asynchronous observers and controllers for discrete-time nonlinear semi-Markov jump singularly perturbed systems subjected to mismatched modes, states, and premise variables between controlled systems and observer-based controllers. Aiming at characterizing nonlinearity with parameter uncertainty, the interval type-2 (IT2) Takagi–Sugeno fuzzy technique is implemented in system modeling. Meanwhile, it is supposed that both observer and controller modes can just be acquired via a hidden Markov mode detector in the first attempt. Then, following the concept of nonparallel distribution compensation, the observer-based IT2 fuzzy asynchronous controllers are constructed with observers and controllers sharing the same fuzzy membership function but different from that in systems, which improves the designed flexibility. In accordance with semi-Markov kernel approach and the Lyapunov function contingent upon both system modes and sojourn times, sufficient criteria are established for the functioning of expected mode-dependent IT2 fuzzy observers and controllers such that the <inline-formula><tex-math>$sigma$</tex-math></inline-formula>-mean-square stability for the resulting nonlinear augmented semi-Markov jump singularly perturbed systems comprised of the controlled systems and observation error systems is guaranteed. Furthermore, from the perspective of fuzzy processing, parameters and relaxation matrices that comply with fuzzy rules are added to ensure system stability while further reducing the conservatism of conditions. Ultimately, a circuit model and comparison examples are shown to substantiate the necessity and superiority of the suggested technique.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3722-3735"},"PeriodicalIF":11.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900429","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