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

Observer-Based SMC for Discrete Interval Type-2 Fuzzy Semi-Markov Jump Models 基于观测器的离散区间2型模糊半马尔可夫跳变模型SMC

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-26 DOI: 10.1109/TFUZZ.2025.3545895

Wenhai Qi;Runkun Li;Peng Shi;Guangdeng Zong

{"title":"Observer-Based SMC for Discrete Interval Type-2 Fuzzy Semi-Markov Jump Models","authors":"Wenhai Qi;Runkun Li;Peng Shi;Guangdeng Zong","doi":"10.1109/TFUZZ.2025.3545895","DOIUrl":"10.1109/TFUZZ.2025.3545895","url":null,"abstract":"This study is devoted to the observer-based sliding mode control (SMC) for discrete nonlinear semi-Markov jump models with incomplete sojourn information and application to the quarter-car suspension model. The nonlinear plant with parameter uncertainty is represented by an interval type-2 fuzzy model, where the membership functions of the fuzzy rules are related to the system mode. Since sojourn information is challenging to obtain in practice, the sojourn time probability density function is considered to be incompletely available. The considered system is more general, not only relaxing the traditional assumption that all the sojourn time probability density functions are completely available, but also covering completely available sojourn time probability density functions as a special case. The main innovation is that an observer-based SMC scheme is designed, which makes the discrete nonlinear models have better dynamic performance, and realizes the reachability of discrete quasi-sliding mode. By the interval type-2 fuzzy and classical Lyapunov function, the mean-square stability criterion of the semi-Markov jump models is constructed employing additional matrix variables. Then, an observer-based SMC mechanism is constructed to achieve the reachability of the quasi-sliding mode. Ultimately, the proposed method is validated by a two-degree-freedom quarter-car suspension model.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1913-1925"},"PeriodicalIF":10.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507308","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

Uncertainty-Aware Superpoint Graph Transformer for Weakly Supervised 3-D Semantic Segmentation 弱监督三维语义分割的不确定性感知叠加点图变换

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-26 DOI: 10.1109/TFUZZ.2025.3543036

Yan Fan;Yu Wang;Pengfei Zhu;Le Hui;Jin Xie;Qinghua Hu

{"title":"Uncertainty-Aware Superpoint Graph Transformer for Weakly Supervised 3-D Semantic Segmentation","authors":"Yan Fan;Yu Wang;Pengfei Zhu;Le Hui;Jin Xie;Qinghua Hu","doi":"10.1109/TFUZZ.2025.3543036","DOIUrl":"10.1109/TFUZZ.2025.3543036","url":null,"abstract":"Weakly supervised 3-D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3-D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intraclass membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1899-1912"},"PeriodicalIF":10.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506994","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

Network Security Scheme for Discrete-Time T-S Fuzzy Nonlinear Active Suspension Systems Based on Multiswitching Control Mechanism 基于多切换控制机制的离散时间T-S模糊非线性主动悬架系统网络安全方案

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-26 DOI: 10.1109/TFUZZ.2025.3545824

Jiaming Shen;Yang Liu;Mohammed Chadli

{"title":"Network Security Scheme for Discrete-Time T-S Fuzzy Nonlinear Active Suspension Systems Based on Multiswitching Control Mechanism","authors":"Jiaming Shen;Yang Liu;Mohammed Chadli","doi":"10.1109/TFUZZ.2025.3545824","DOIUrl":"10.1109/TFUZZ.2025.3545824","url":null,"abstract":"This article investigates the cybersecurity problem of active suspension systems (ASSs) subject to random denial-of-service (DoS) attacks. Consider the nonlinearity and uncertainty of ASSs, the Takagi–Sugeno (T-S) fuzzy theory is applied to address these issues. In order to model various potential operating behaviors of the system, a high-order multiswitching-mode (HOMSM) T-S fuzzy control scheme is constructed, in which a series of free-weighted matrix sets are developed for different switching modes, such that the conservatism of controller design is reduced to a certain extent. By designing the HOMSM control method, a certain level of resistance to randomly activated DoS attacks can be achieved. With the help of homogeneous polynomial technology, several time-varying balance matrices are constructed for extracting the properties of different switching modes. Then, the exponential stability conditions of ASSs under DoS attacks can be derived, and the <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> performance criterion is guaranteed. Finally, the theoretical results are validated by the hardware-in-the-loop experiments.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1926-1936"},"PeriodicalIF":10.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506992","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

Fully Informed Fuzzy Logic System Assisted Adaptive Differential Evolution Algorithm for Noisy Optimization 全信息模糊逻辑系统辅助下的自适应差分进化噪声优化算法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-25 DOI: 10.1109/TFUZZ.2025.3545442

Sheng Xin Zhang;Yu Hong Liu;Xin Rou Hu;Li Ming Zheng;Shao Yong Zheng

引用次数: 0

Impulsive Control of Nonlinear Multiagent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy 非线性多智能体系统的脉冲控制：一种模糊自适应与事件触发的混合策略

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-25 DOI: 10.1109/TFUZZ.2025.3545740

Fang Han;Hai Jin

{"title":"Impulsive Control of Nonlinear Multiagent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy","authors":"Fang Han;Hai Jin","doi":"10.1109/TFUZZ.2025.3545740","DOIUrl":"10.1109/TFUZZ.2025.3545740","url":null,"abstract":"In this article, we present a hybrid control approach that integrates an adaptive fuzzy mechanism with an event-triggered impulse strategy to address consensus control challenges in nonlinear <italic>multiagent systems</i> (MASs) with uncertain information, both in leader and leaderless scenarios. First, a fuzzy logic-based adaptive control mechanism is proposed to handle system nonlinearity and uncertainty. This mechanism dynamically adjusts fuzzy control rules in real time, enabling the system to adapt to changing conditions and enhancing its robustness against nonlinear disturbances. Second, to alleviate communication overhead and reduce control frequency, an event-triggered impulse strategy is implemented. This strategy activates impulse control only under specific conditions, effectively minimizing control actions while maintaining system consensus. Using Lyapunov stability theory, sufficient conditions for achieving system consensus are established, demonstrating the global consensus and robustness of the MASs under certain criteria. Finally, numerical simulations and experimental evaluations validate the proposed method's effectiveness and its practical advantages.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1889-1898"},"PeriodicalIF":10.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495345","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

Robust Divide-and-Conquer Multiple Importance Kalman Filtering via Fuzzy Measure for Multipassive-Sensor Target Tracking 基于模糊测度的鲁棒分治多重要卡尔曼滤波多被动传感器目标跟踪

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-18 DOI: 10.1109/TFUZZ.2025.3542090

Hongwei Zhang

{"title":"Robust Divide-and-Conquer Multiple Importance Kalman Filtering via Fuzzy Measure for Multipassive-Sensor Target Tracking","authors":"Hongwei Zhang","doi":"10.1109/TFUZZ.2025.3542090","DOIUrl":"10.1109/TFUZZ.2025.3542090","url":null,"abstract":"Multipassive-sensor target tracking systems (MPSTTs) enable noncontact and covert acquisition of the target status, with bearings processing being a prerequisite for accurately predicting the target behavior over large spatial ranges. However, MPSTTs are prone to fuzziness owing to coordinate coupled and Gaussian truncation errors. To address these challenges, we propose a high-precision robust tracker that uses divide-and-conquer multiple-importance Kalman filtering (DCMIKF) via a fuzzy measure. Specifically, we designed coupling maneuver models and attached their model-truth probabilities as a fuzzy set. The measurements-to-target data association was then formulated as a fuzzy maxima searching problem with soft spatiotemporal causal constraints. The proposed approach enables the integration of all available information to derive the fuzzy measure, dividing the hybrid state estimation into variable-structure model estimation and model-conditioned filtering. Simultaneously, DCMIKF combines importance prediction with constrained convex measurement updating using KF and fuses the outputs via fuzzy model probability. Besides, it offers a purely mathematical approach for quantifying the average fuzziness yielded from the fuzzy set, thereby smoothing the mismatches between multilikelihoods, proposal distribution, and target posterior. Simulated and measured results conformed well; compared with the regression Gaussian process motion tracker, DCMIKF overcame the prior hypotheses in machine learning, reducing the problematic cubic complexity in the number of time steps to the linear time complexity; compared to the interacting multiple model minimax particle filter, DCMIKF significantly improves filtering accuracy and tracking robustness.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1864-1875"},"PeriodicalIF":10.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443473","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

Differentially Private Distributed Nash Equilibrium Seeking for Aggregative Games With Linear Convergence 线性收敛聚集对策的差分私有分布纳什均衡寻求

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-14 DOI: 10.1109/TFUZZ.2025.3542497

Ying Chen;Qian Ma;Peng Jin;Shengyuan Xu

{"title":"Differentially Private Distributed Nash Equilibrium Seeking for Aggregative Games With Linear Convergence","authors":"Ying Chen;Qian Ma;Peng Jin;Shengyuan Xu","doi":"10.1109/TFUZZ.2025.3542497","DOIUrl":"10.1109/TFUZZ.2025.3542497","url":null,"abstract":"The problem of differentially private distributed Nash equilibrium seeking for aggregative games with unknown nonlinear players under unbalanced directed graphs is investigated in this article. We utilize an auxiliary variable to eliminate the influence of the unbalancedness caused by the topological structure. The consensus protocol is employed to estimate the aggregate function. To protect players' sensitive information, Laplace noise is added on shared messages among players to perturb the raw information. By combining with the heavy-ball method, Nesterov gradient descent strategy and the consensus protocol, a momentum-based auxiliary system is designed to generate the Nash equilibrium signals with differential privacy guarantee with the help of the fixed gradient step size. A weakening factor is adopted to ensure the almost sure convergence of the designed auxiliary system in the presence of noise. Then, we analyze the linear convergence of the auxiliary system to the Nash equilibrium by using the linear systems inequalities. The rigorous differential privacy with a finite cumulative privacy budget without requiring a tradeoff between the convergence accuracy and differential privacy is also proven. In addition, an adaptive fuzzy method is used to deal with the unknown nonlinear dynamics, and the controller is designed to drive the actions of the players to the neighborhood of Nash equilibrium with the aid of auxiliary system. Finally, we present a numerical example to present the effectiveness of the proposed algorithm.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1853-1863"},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417497","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 Domain Adaptation From Heterogeneous Source Teacher Models 异构源教师模型的模糊域自适应

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-11 DOI: 10.1109/TFUZZ.2025.3541001

Keqiuyin Li;Jie Lu;Hua Zuo;Guangquan Zhang

{"title":"Fuzzy Domain Adaptation From Heterogeneous Source Teacher Models","authors":"Keqiuyin Li;Jie Lu;Hua Zuo;Guangquan Zhang","doi":"10.1109/TFUZZ.2025.3541001","DOIUrl":"10.1109/TFUZZ.2025.3541001","url":null,"abstract":"Unsupervised domain adaptation that relies on data matching can raise privacy concerns when leveraging transferable knowledge from the source domain(s). To address this issue, source-free domain adaptation has been proposed and subsequently developed. However, privacy risks persist due to potential data leaks from model inversion attacks even when using only source models without accessing the source data directly. Alongside this, another underexplored problem is feature heterogeneity across multiple source domains. In this article, we propose a fuzzy domain adaptation method, fuzzy heterogeneous domain adaptation (FuzHDA), that learns fuzzy rules from heterogeneous teacher models, even when these models are black boxes. The proposed method first trains heterogeneous source models privately on individual devices without sharing any information, including neither data nor model parameters. Then, using a memory bank that stores all target predictions from the pretrained source models, we apply a self-knowledge distillation approach to train a target model, which simulates the predictions from these heterogeneous source teachers. After completing the target model, a cross-modality hybrid model leveraging prompt learning and uncovering causal factors is fine-tuned via self-supervision at the last step to facilitate transfer performance. Experiments on real-world datasets demonstrate the superiority of the proposed FuzHDA.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1840-1852"},"PeriodicalIF":10.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393234","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

Semisupervised Feature Selection With Multiscale Fuzzy Information Fusion: From Both Global and Local Perspectives 基于多尺度模糊信息融合的半监督特征选择：全局和局部视角

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-11 DOI: 10.1109/TFUZZ.2025.3540884

Nan Zhou;Shujiao Liao;Hongmei Chen;Weiping Ding;Yaqian Lu

{"title":"Semisupervised Feature Selection With Multiscale Fuzzy Information Fusion: From Both Global and Local Perspectives","authors":"Nan Zhou;Shujiao Liao;Hongmei Chen;Weiping Ding;Yaqian Lu","doi":"10.1109/TFUZZ.2025.3540884","DOIUrl":"10.1109/TFUZZ.2025.3540884","url":null,"abstract":"In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multiscale data have received widespread attention due to its rich knowledge representation. However, current research on multiscale data primarily focuses on supervised learning environments, while semisupervised feature selection for multiscale data with limited labels remains inadequately explored. Meanwhile, existing studies on multiscale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multiscale fuzzy information fusion mechanism, this article proposes two new semisupervised feature selection approaches for multiscale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multiscale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semisupervised feature selection algorithms are developed based on global and local multiscale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multiscale data with limited labels and exhibit superior classification performance, computational efficiency, and robustness.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1825-1839"},"PeriodicalIF":10.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393233","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

Adaptive Fuzzy Attention Inference to Control a Microgrid Under Extreme Fault on Grid Bus 网格总线极端故障下微电网的自适应模糊注意力推理控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-02-06 DOI: 10.1109/TFUZZ.2025.3539325

Tanvir M. Mahim;A.H.M.A. Rahim;M. Mosaddequr Rahman

{"title":"Adaptive Fuzzy Attention Inference to Control a Microgrid Under Extreme Fault on Grid Bus","authors":"Tanvir M. Mahim;A.H.M.A. Rahim;M. Mosaddequr Rahman","doi":"10.1109/TFUZZ.2025.3539325","DOIUrl":"10.1109/TFUZZ.2025.3539325","url":null,"abstract":"Power quality of a microgrid falls due to large penetration in renewable sources with rapid fluctuations. This article develops a double Q Learning (DQL) based adaptive fuzzy attention inference (FAI) to control a microgrid connected to the grid. Detailed dynamic modeling is presented with renewable sources such as photovoltaic (PV), wind, fuel, and microalternators with respective power electronics interfaces. The inference scheme controls the phase angle of the static compensator (STATCOM) coupled with capacitive energy storage device in the microgrid. STATCOM provides reactive power, while the capacitive storage corrects real power imbalances. Numerical results showed the developed control scheme restores stability rapidly in the event of severe symmetrical three-phase to ground fault on the grid bus. Rule base and corresponding membership function of the inference dynamically adapts based on the DQL mechanics. Prioritized experience and shallow neural net reward model are integrated where importance sampling of the temporal difference based rewards, contributes to overcome microgrid transients. The proposed control outperform conventional optimized proportional-integral-derivative (PID), model predictive, and sliding mode control schemes to restore normal operation of the converters across different levels of electromechanical transients.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"1815-1824"},"PeriodicalIF":10.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258497","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