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

Dynamic Fuzzy Sampler for Graph Neural Networks 图神经网络的动态模糊采样器

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-06 DOI: 10.1109/TFUZZ.2024.3509018

Jia Wei;Xingjun Zhang;Witold Pedrycz;Weiping Ding

{"title":"Dynamic Fuzzy Sampler for Graph Neural Networks","authors":"Jia Wei;Xingjun Zhang;Witold Pedrycz;Weiping Ding","doi":"10.1109/TFUZZ.2024.3509018","DOIUrl":"10.1109/TFUZZ.2024.3509018","url":null,"abstract":"Graph Neural Networks (GNNs) are widely used across fields, with inductive learning replacing transductive learning as the mainstream training paradigm due to its superior memory efficiency, computation speed, and generalization. Neighbor node sampling, a key step in inductive learning, is critical to model performance. However, existing samplers focus only on adjacency matrix-based sampling, neglecting the varying impacts of different neighbors on target nodes over time. They usually aggregate the neighbor information in a simple way such as averaging or summing, which limits the information representation, robustness, and generalization. To address these limitations, we propose a Dynamic Fuzzy Sampler (DFS) based on a Gaussian fuzzy system. DFS accounts for node diversity and models the uncertainties and fuzziness in neighbor-target mutual information dynamically. Specifically, DFS first innovatively constructs a learnable Gaussian fuzzy set system for determining the membership degree of different neighbors to the target node at different moments. Subsequently, DFS aggregates the target node embeddings and membership-weighted neighbor embeddings to update the target node's features, which makes the target node utilize the sampling information more effectively. The aggregated target node effectively captures the graph structure information and neighbor node information, which can facilitate the subsequent graph neural network-based graph representation model with stronger representation and generalization capabilities. Experimental results on supervised and self-supervised graph datasets demonstrate that DFS consistently outperforms state-of-the-art sampling schemes. DFS achieves up to 1.90% and 9.52% F1-score improvement compared to the state-of-the-art schemes on small- and large-scale graphs, respectively.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1357-1368"},"PeriodicalIF":10.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934629","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

Small-Gain-Based Fixed-Time Faulty Parameter Estimation for the Interconnected Fuzzy Systems With Multiple Time-Varying Delays 多时变时滞互联模糊系统的小增益定时故障参数估计

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-03 DOI: 10.1109/TFUZZ.2024.3520347

Ke Zhang;Qingyi Liu;Bin Jiang

引用次数: 0

Event-Triggered Trajectory Tracking Control for Unmanned Surface Vessels With Prescribed Performance Using Barrier Lyapunov Functions 基于Barrier Lyapunov函数的规定性能无人水面舰艇事件触发轨迹跟踪控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-03 DOI: 10.1109/TFUZZ.2025.3525701

Xian Du;Xu Yuan;Bin Yang;Xudong Zhao

{"title":"Event-Triggered Trajectory Tracking Control for Unmanned Surface Vessels With Prescribed Performance Using Barrier Lyapunov Functions","authors":"Xian Du;Xu Yuan;Bin Yang;Xudong Zhao","doi":"10.1109/TFUZZ.2025.3525701","DOIUrl":"10.1109/TFUZZ.2025.3525701","url":null,"abstract":"This article investigates the event-triggered trajectory tracking control for unmanned surface vessels (USVs) with prescribed performance subjected to asymmetric time-varying state constraints. In order to realize the prescribed-time control task, a performance function is introduced and the fuzzy logic systems are adopted to estimate the unknown nonlinearities of the USVs model. Subsequently, a flexible event-triggered mechanism is designed to minimize the wastage of communication resource, taking into account the continuous updating of the actual control input. A practical virtual control signal is devised and used for backstepping design. On this basis, an event-triggered adaptive fuzzy trajectory tracking control algorithm with prescribed performance is developed by combining the defined performance function and the barrier Lyapunov function approach, which not only solves the asymmetric time-varying state constraints of the USVs, but also ensures that the USVs achieve the predetermined tracking performance, meanwhile, all the closed-loop signals can be maintained bounded. Finally, the simulation results are performed to demonstrate the feasibility of the developed control scheme.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1466-1474"},"PeriodicalIF":10.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924716","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

Secure Control for T–S Fuzzy Wind Turbine Systems Under Hybrid Cyberattacks via an Adaptive Memory Event-Triggered Mechanism 基于自适应记忆事件触发机制的混合网络攻击下T-S模糊风力发电系统安全控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-03 DOI: 10.1109/TFUZZ.2025.3525778

Dong Xu;Yajuan Liu;Sangmoon Lee

{"title":"Secure Control for T–S Fuzzy Wind Turbine Systems Under Hybrid Cyberattacks via an Adaptive Memory Event-Triggered Mechanism","authors":"Dong Xu;Yajuan Liu;Sangmoon Lee","doi":"10.1109/TFUZZ.2025.3525778","DOIUrl":"10.1109/TFUZZ.2025.3525778","url":null,"abstract":"This article addresses the secure control problem for the Takagi–Sugeno (T–S) fuzzy wind turbine system (WTS) subject to hybrid cyberattacks. To reduce system performance loss and redundant data transmission under such attacks, a novel adaptive memory event-triggered mechanism (ETM) is proposed, offering two key advantages. First, unlike traditional ETMs that rely solely on current system information, the adaptive memory ETM utilizes historical release data to adjust communication frequency and enhance control effectiveness. Second, an attack-related triggering condition and adaptive law are introduced to reduce the abnormaldata transmission induced by denial of service (DoS) attacks and extend the lifespan of the sensor node. Sufficient conditions are derived to ensure the mean-square <inline-formula><tex-math>$mathcal {H}_{infty }$</tex-math></inline-formula> asymptotic stability of the resulting T–S fuzzy WTS, while also guaranteeing uniformly ultimate boundedness in the presence of DoS attacks. Finally, an illustrative example is used to show the effectiveness of the proposed method.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1475-1487"},"PeriodicalIF":10.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924717","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 Adaptive Group Formation-Containment Tracking Control of Nonlinear Multiagent Systems With Intermittent Actuator Faults 具有间歇执行器故障的非线性多智能体系统的模糊自适应群形成-容纳跟踪控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-02 DOI: 10.1109/TFUZZ.2025.3525481

Zhibin Zhu;Yunbiao Jiang;Zhongxin Liu;Fuyong Wang

引用次数: 0

Fuzzy Quantum Group Decision Making and Its Application in Meteorological Disaster Emergency 模糊量子群决策及其在气象灾害应急中的应用

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-02 DOI: 10.1109/TFUZZ.2024.3525009

Shuli Yan;Yizhao Xu;Zaiwu Gong;Enrique Herrera-Viedma

引用次数: 0

Predefined-Time Fuzzy Formation Control for High-Order Multiagent Systems via Event-Triggered Schemes 基于事件触发方案的高阶多智能体系统的预定义时间模糊编队控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-01 DOI: 10.1109/TFUZZ.2024.3524716

Jiawei Ma;Huaguang Zhang;Juan Zhang;Lei Wan

引用次数: 0

Adjacency-Aware Fuzzy Label Learning for Skin Disease Diagnosis 邻接感知模糊标签学习用于皮肤病诊断

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-30 DOI: 10.1109/TFUZZ.2024.3524250

Murong Zhou;Baifu Zuo;Guohua Wang;Gongning Luo;Fanding Li;Suyu Dong;Wei Wang;Kuanquan Wang;Xiangyu Li;Lifeng Xu

{"title":"Adjacency-Aware Fuzzy Label Learning for Skin Disease Diagnosis","authors":"Murong Zhou;Baifu Zuo;Guohua Wang;Gongning Luo;Fanding Li;Suyu Dong;Wei Wang;Kuanquan Wang;Xiangyu Li;Lifeng Xu","doi":"10.1109/TFUZZ.2024.3524250","DOIUrl":"10.1109/TFUZZ.2024.3524250","url":null,"abstract":"Automatic acne severity grading is crucial for the accurate diagnosis and effective treatment of skin diseases. However, the acne severity grading process is often ambiguous due to the similar appearance of acne with close severity, making it challenging to achieve reliable acne severity grading. Following the idea of fuzzy logic for handling uncertainty in decision-making, we transforms the acne severity grading task into a fuzzy label learning (FLL) problem, and propose a novel adjacency-aware fuzzy label learning (AFLL) framework to handle uncertainties in this task. The AFLL framework makes four significant contributions, each demonstrated to be highly effective in extensive experiments. First, we introduce a novel adjacency-aware decision sequence generation method that enhances sequence tree construction by reducing bias and improving discriminative power. Second, we present a consistency-guided decision sequence prediction method that mitigates error propagation in hierarchical decision-making through a novel selective masking decision strategy. Third, our proposed sequential conjoint distribution loss innovatively captures the differences for both high and low fuzzy memberships across the entire fuzzy label set while modeling the internal temporal order among different acne severity labels with a cumulative distribution, leading to substantial improvements in FLL. Fourth, to the best of our knowledge, AFLL is the first approach to explicitly address the challenge of distinguishing adjacent categories in acne severity grading tasks. Experimental results on the public ACNE04 dataset demonstrate that AFLL significantly outperforms existing methods, establishing a new state-of-the-art in acne severity grading.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1429-1440"},"PeriodicalIF":10.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905572","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

Event-Triggered Data-Driven Iterative Learning Control for Nonlinear MASs Under Switching Topologies 切换拓扑下非线性质量的事件触发数据驱动迭代学习控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-26 DOI: 10.1109/TFUZZ.2024.3523127

Shanshan Sun;Yuan-Xin Li;Zhongsheng Hou

{"title":"Event-Triggered Data-Driven Iterative Learning Control for Nonlinear MASs Under Switching Topologies","authors":"Shanshan Sun;Yuan-Xin Li;Zhongsheng Hou","doi":"10.1109/TFUZZ.2024.3523127","DOIUrl":"10.1109/TFUZZ.2024.3523127","url":null,"abstract":"This article aims to address the problem of distributed model-free adaptive iterative learning control for nonlinear discrete-time multiagent systems under switching topologies. To save valuable bandwidth in the wireless channel without sacrificing system performance, an event-triggered iterative learning control strategy is established and employed, where information is only transmitted at triggered instants. First, by virtue of the dynamic linearization technology, the controlled system can be converted into a linear model to construct the controller structure. Second, a model-free adaptive iterative learning consensus control scheme is proposed merely employing the input and output data, in which better tracking performance can be attained by learning the previous experience. Third, a dynamic event-triggered mechanism along the iteration domain is set up to deal with the limited bandwidth issue, effectively saving communication resources. Unlike most model-free adaptive control results, the constructed distributed controller is designed based on controller-dynamic-linearization approach to deal with the controller structure design issue without designing the cost function, making it more convenient in solving tracking control issues for multiagent systems under iteration-switching communication topologies, which is more suitable for the actual environment. Using graph theory and the contraction mapping principle, the convergence of tracking control errors is theoretically analyzed. Ultimately, the effectiveness of the established control schemes is illustrated through two simulation examples.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1322-1332"},"PeriodicalIF":10.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888267","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 Generalized Integrated Fuzzy-MPC With Optimal Input Excitation for Complex Systems With Industrial Applications 具有工业应用的复杂系统的最优输入激励广义集成模糊- mpc

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-25 DOI: 10.1109/TFUZZ.2024.3522339

Keke Huang;Xinyu Ying;Dehao Wu;Chunhua Yang;Weihua Gui

{"title":"A Generalized Integrated Fuzzy-MPC With Optimal Input Excitation for Complex Systems With Industrial Applications","authors":"Keke Huang;Xinyu Ying;Dehao Wu;Chunhua Yang;Weihua Gui","doi":"10.1109/TFUZZ.2024.3522339","DOIUrl":"10.1109/TFUZZ.2024.3522339","url":null,"abstract":"Complex systems are frequently influenced by uncertain factors, making it difficult for traditional fixed-model control schemes to achieve high-precision control. Data-driven control methods offer a solution, but they face challenges in constructing accurate models due to insufficient excitation in operational data. Moreover, mismatches between historical models and new conditions coupled with limited data accumulation under new conditions reduces the operational performance throughout the entire process. To address these issues, this paper proposes a generalized integrated fuzzy model predictive control (GIF-MPC) framework. It combines the generalization capability of fuzzy control with the precision of model predictive control to ensure highprecision control under all conditions. Specifically, a strategy switching mechanism, triggered by a mismatch characteristic parameter is first proposed, which transitions the original strategy to a fuzzy-driven excitation control method, thereby mitigating the control performance degradation caused by the mismatch between control strategies and complex systems. Then, a fuzzy control feature extraction method is proposed to balance fuzzy set activation and improve adaptability to unknown conditions. Additionally, an optimal input excitation design method is proposed to tackle insufficient data excitation, enabling effective control. Once sufficient data is accumulated, the model switches to model predictive control. The dual decision mechanism guided by the data information and triggered by the mismatch characteristic parameter effectively ensures high precision control under uncertainties. Numerical experiments demonstrate that the GIF-MPC method ensures high-precision control throughout disturbances and condition changes. The solution is also successfully deployed in an industrial setting, validating its excellent control performance under full operation conditions.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1415-1428"},"PeriodicalIF":10.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888072","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