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

Dissipativity-Based Integral Sliding Mode Control of Fuzzy Switched Positive Systems With Bumpless Transfer Performance 具有无碰撞传递性能的模糊开关正系统的耗散积分滑模控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-01 DOI: 10.1109/TFUZZ.2025.3584947

Zhiye Bai;Baowei Wu;Yue-E Wang;Heng Liu

{"title":"Dissipativity-Based Integral Sliding Mode Control of Fuzzy Switched Positive Systems With Bumpless Transfer Performance","authors":"Zhiye Bai;Baowei Wu;Yue-E Wang;Heng Liu","doi":"10.1109/TFUZZ.2025.3584947","DOIUrl":"10.1109/TFUZZ.2025.3584947","url":null,"abstract":"Nonlinear switched positive systems (SPSs) subject to time-varying delays are ubiquitous in actual applications, whose control and synthesis are complicated due to the influence of positivity constraints, switching laws, and time delays. This article addresses the issues of dissipative sliding mode control and dissipativity-based sliding mode tracking control for fuzzy SPSs in the presence of time-varying delays under a state-dependent switching paradigm. First, a bumpless transfer technique and a hysteresis switching mechanism for fuzzy switched systems are employed to mitigate input chattering and avoid high-frequency switching, respectively. Then, a novel fuzzy integral switching sliding surface is formulated to accommodate the characteristics of nonlinear switched systems. Based on the multiple linear copositive Lyapunov function, sufficient conditions are derived to guarantee that the closed-loop positive system is asymptotically stable with strict <inline-formula><tex-math>$(q,r)$</tex-math></inline-formula>-<inline-formula><tex-math>$alpha$</tex-math></inline-formula> dissipativity and bumpless transfer performance under the proposed controller. Furthermore, the dissipative tracking control design is investigated via the designed fuzzy integral sliding mode surface, with guaranteed reachability under positivity and stability conditions. Ultimately, the feasibility of the mentioned fuzzy sliding mode control strategy is verified through two examples with a Lotka–Volterra population model.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3182-3194"},"PeriodicalIF":11.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533134","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

Robust Federated Fuzzy C-Means Algorithm in Heterogeneous Scenarios 异构场景下的鲁棒联邦模糊c均值算法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-30 DOI: 10.1109/TFUZZ.2025.3584697

Qixian Zhang;Zhaohong Deng;Wei Zhang;Zhuangzhuang Zhao;Zhiyong Xiao;Kup-Sze Choi;Guanjin Wang;Yuxi Ge;Shudong Hu

{"title":"Robust Federated Fuzzy C-Means Algorithm in Heterogeneous Scenarios","authors":"Qixian Zhang;Zhaohong Deng;Wei Zhang;Zhuangzhuang Zhao;Zhiyong Xiao;Kup-Sze Choi;Guanjin Wang;Yuxi Ge;Shudong Hu","doi":"10.1109/TFUZZ.2025.3584697","DOIUrl":"10.1109/TFUZZ.2025.3584697","url":null,"abstract":"The federated fuzzy C-means (federated FCM) extends the traditional Fuzzy C-means (FCM) to the federated learning (FL) scenario, aiming to address the data privacy preservation issue of soft clustering in distributed environments. However, a significant challenge persists with existing federated FCM algorithms, i.e., they struggle to converge effectively in complex heterogeneous scenarios, leading to unstable clustering outcomes. Here the complex heterogeneous scenarios stem from the combination of nonindependently and identically distributed (non-IID) data across different clients (statistical heterogeneity), coupled with the involvement of only some clients in each iteration (systematic heterogeneity). While prior research has attempted to address the impact of statistical heterogeneity in FL scenarios, it has overlooked the issue of system heterogeneity. In response, this article proposes a novel federated FCM algorithm (SC-FFCM) that remains robust even in such complex heterogeneous scenarios. First, the client-side clustering module of SC-FFCM adopts a gradient-based FCM algorithm, facilitating corrections to the direction of local optimization. Second, the algorithm introduces a control variates technique to rectify update bias during the iteration process, thereby mitigating the adverse effects of random client sampling and non-IID data distribution on the algorithm convergence. Finally, the proposed algorithm approximates the ideal federated FCM algorithm. Experimental studies verify the effectiveness of the proposed method.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3168-3181"},"PeriodicalIF":11.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520688","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

FCAformer: Fuzzy-Enhanced Class-Aware Attention Based Transformer for Weakly Supervised Histopathology Image Segmentation FCAformer：用于弱监督组织病理学图像分割的基于模糊增强类别感知注意力的转换器

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-30 DOI: 10.1109/TFUZZ.2025.3583819

Xiaotian Cheng;Weiping Ding;Jiashuang Huang;Hengrong Ju;Tianyi Zhou;Jing Guo;Witold Pedrycz

{"title":"FCAformer: Fuzzy-Enhanced Class-Aware Attention Based Transformer for Weakly Supervised Histopathology Image Segmentation","authors":"Xiaotian Cheng;Weiping Ding;Jiashuang Huang;Hengrong Ju;Tianyi Zhou;Jing Guo;Witold Pedrycz","doi":"10.1109/TFUZZ.2025.3583819","DOIUrl":"10.1109/TFUZZ.2025.3583819","url":null,"abstract":"Pixel-level histopathology image segmentation plays a vital role in computational pathology, and weakly supervised segmentation methods, which rely solely on image-level labels, have shown great potential. However, most existing weakly supervised segmentation methods are limited by the fixed receptive field of convolutional neural networks, and overlook the uncertainty of the distribution of different tissue types and the fuzziness of class boundaries, resulting in limited segmentation effects. To address these problems, we propose a fuzzy-enhanced class-aware attention based Transformer (FCAformer) for weakly supervised histopathology image segmentation. FCAformer employs the Transformer architecture for model global contextual information, which effectively alleviates the limitation of fixed receptive field on the size of attention map in traditional methods. Subsequently, FCAformer integrates fuzzy system to model the uncertainty in histopathology images. Specifically, it assigns membership function to the output feature map of the last layer of Transformer encoder, generates fuzzy membership matrix, extracts fuzzy features by combining three fuzzy rules, and finally fuses these features to generate fuzzy attention map. This attention map guides the network to learn the characteristics of different tissue types and improve the fuzziness of class boundaries, thereby improving the modeling ability of the model on uncertain tissue distribution and fuzzy areas. In addition, based on the idea of contrastive learning, we design contrastive class token (CCT) loss to further enhance the distinguishability between different class labels. Extensive experiments on LUAD-HistoSeg and BCSS-WSSS datasets demonstrate that FCAformer achieves state-of-the-art segmentation performance in weakly supervised segmentation tasks.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3118-3132"},"PeriodicalIF":11.9,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520689","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 Comprehensive Evaluation for Operating Load and Efficiency of Semiautogenous Grinding Process 半自磨工艺运行负荷与效率的模糊综合评价

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-27 DOI: 10.1109/TFUZZ.2025.3583874

Zhenhong Liao;Jinhua She;Wen Chen;Min Wu;Yanglong Zhang

{"title":"Fuzzy Comprehensive Evaluation for Operating Load and Efficiency of Semiautogenous Grinding Process","authors":"Zhenhong Liao;Jinhua She;Wen Chen;Min Wu;Yanglong Zhang","doi":"10.1109/TFUZZ.2025.3583874","DOIUrl":"10.1109/TFUZZ.2025.3583874","url":null,"abstract":"Semiautogenous grinding (SAG) processes are widely used in mineral processing. In industrial production, operators often lack a comprehensive and objective assessment of the operating conditions of an SAG process and usually make different control decisions based on experience to meet production demands. This article explains a two-level fuzzy comprehensive evaluation method for the load and efficiency of an SAG process. First, the operation mechanism of an SAG process is analyzed, and the evaluation indicators of load and efficiency are determined. Then, the levels and weights of each indicator are determined by combining expert experience and actual production data. Finally, the operating load level is first evaluated by combining the information entropy weight and fuzzy computation. When the load level is within an acceptable range, a fuzzy comprehensive evaluation of the efficiency level is then performed. The modeling method of efficiency indicator, circulating load ratio (CLR), is integrated with random forest and Bayesian optimization to carry out the efficiency evaluation. Experiments using actual production data demonstrate CLR modeling performance with a mean absolute error of 1.084% and a mean absolute percentage error of 9.415%. The accuracy of the operating efficiency evaluation is 81.69%. This method provides an objective and uniform criterion for operators to evaluate the operating load and efficiency of an SAG process, thus guiding the regulation of actual production.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3145-3155"},"PeriodicalIF":11.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503687","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

Finite-Time $mathcal {H}_{infty }$ Event-Triggered SMC for T–S Fuzzy UMVs Under Aperiodic DoS Attacks 非周期DoS攻击下T-S模糊虚拟机的有限时间$mathcal {H}_{infty }$事件触发SMC

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-27 DOI: 10.1109/TFUZZ.2025.3583883

Jiangming Xu;Peng Cheng;Xiang Zhang;Jun Cheng;Zehua Jia;Weidong Zhang

引用次数: 0

Integrating Indirect Preferences Into Ordinal Regression Estimation of Missing Values and Consensus Reaching 将间接偏好纳入缺失值的有序回归估计与共识达成

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-26 DOI: 10.1109/TFUZZ.2025.3582248

Xuan Xia;Zaiwu Gong;Jeffrey Yi-Lin Forrest;Kun Zhou;Enrique Herrera-Viedma

{"title":"Integrating Indirect Preferences Into Ordinal Regression Estimation of Missing Values and Consensus Reaching","authors":"Xuan Xia;Zaiwu Gong;Jeffrey Yi-Lin Forrest;Kun Zhou;Enrique Herrera-Viedma","doi":"10.1109/TFUZZ.2025.3582248","DOIUrl":"10.1109/TFUZZ.2025.3582248","url":null,"abstract":"Constraints from the external environment and personal experiences may hinder decision makers from offering complete fuzzy preference relations in situations of group decision making. Conflicts in individual different preferences impede any effort of reaching consensus. Addressing such challenges, traditional methods typically isolate fuzzy preference relation completion and consensus reaching as two separate parts, while improving consistency level through iterative processes. However, such an approach affects the efficiency of consensus achievement. Furthermore, existing studies utilize limited known objective information for filling missing values, while neglecting the excavation of indirect preferences that requires lower cognitive capacity from decision makers. Consequently, the credibility of decision results is greatly reduced. Through integrating indirect preferences, this article introduces a consensus framework that concurrently accomplishes fuzzy preference relation completion and consensus reaching. In the framework, an ordinal regression consensus discriminant model is constructed to first assess the compatibility of information sets. Subsequently, to accurately identify and manage conflicting information, we design a conflict feedback mechanism by considering the minimum cost elimination consensus model and the minimum cost adjustment consensus model. Our proposed method not only deeply explores decision makers’ implicit preferences, but also achieves the integration of three goals: missing value prediction, consensus reaching, and additive consistency realization. It avoids multiple consistency iterations and distortions, while enhancing the reliability of estimated values and the efficiency of consensus reaching. Finally, the validity of the proposed method is demonstrated through case analysis, sensitivity analysis, and comparative analysis.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3076-3090"},"PeriodicalIF":11.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500693","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 Humanized Sentiment Knowledge Enhanced Dependency Graphs Implementing with Aspect-based Sentiment Analysis 基于方面的情感分析实现的模糊人性化情感知识增强依赖图

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-25 DOI: 10.1109/tfuzz.2025.3583050

Xuefeng Shi, Weiping Ding, Min Hu, Xin Kang, Fuji Ren

引用次数: 0

Anisotropic-Gaussian-Kernel-Based Fuzzy Clustering Algorithm for Feature Selection 基于各向异性高斯核的模糊聚类特征选择算法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-20 DOI: 10.1109/TFUZZ.2025.3581918

Jun Liu;Mengyuan Wu;Mingwei Lin;Zeshui Xu

{"title":"Anisotropic-Gaussian-Kernel-Based Fuzzy Clustering Algorithm for Feature Selection","authors":"Jun Liu;Mengyuan Wu;Mingwei Lin;Zeshui Xu","doi":"10.1109/TFUZZ.2025.3581918","DOIUrl":"10.1109/TFUZZ.2025.3581918","url":null,"abstract":"Soft clustering algorithms based on fuzzy C-means (FCM) have been extensively applied to complex data analysis. However, existing FCM variants still encounter key limitations: a large number of iterations due to slow convergence on high-dimensional data, equal weights of all samples, which increases sensitivity to noise, and strong dependence on empirically chosen fuzzy parameters, often resulting in suboptimal solutions. In order to address these challenges, in this work, we propose an adaptive FCM clustering algorithm based on anisotropic Gaussian kernel function, which facilitates the process of feature selection for multidimensional data with fewer iterations after feature reduction based on updating the kernel width vector. In order to enhance the classification accuracy, we assign adaptive weights to each sample and adjust these weights to mitigate the impact of outliers on classification accuracy. Unlike traditional fuzzy clustering algorithms, we employ the particle swarm optimization algorithm with time-varying acceleration coefficients to determine the global optimal parameters, thus reducing the computational resources and enhancing the algorithm’s efficiency. The experimental results based on 16 publicly available datasets validate that the proposed algorithm achieves higher classification accuracy with minimal number of iterations.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3061-3075"},"PeriodicalIF":11.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335019","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

Model-Free Neuro-Fuzzy Q-Learning Control With Swarm Intelligence 基于群体智能的无模型神经模糊q -学习控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-19 DOI: 10.1109/TFUZZ.2025.3581421

Ding Wang;Zeqiang Yuan;Ao Liu;Qiao Lin;Junfei Qiao

{"title":"Model-Free Neuro-Fuzzy Q-Learning Control With Swarm Intelligence","authors":"Ding Wang;Zeqiang Yuan;Ao Liu;Qiao Lin;Junfei Qiao","doi":"10.1109/TFUZZ.2025.3581421","DOIUrl":"10.1109/TFUZZ.2025.3581421","url":null,"abstract":"In this article, a novel neuro-fuzzy-based evolution-guided Q-learning (EGQL) algorithm is established for solving the optimal control problem of unknown nonlinear systems. To enhance the accuracy for approximating the Q-function, the adaptive neuro-fuzzy inference system (ANFIS) is leveraged, which offers superior precision compared to traditional polynomial approximations commonly used in adaptive dynamic programming. Despite its advantages, the ANFIS-based approximation faces challenges in obtaining the derivative of the Q-function with respect to the control input. To address this limitation, evolutionary algorithms are integrated into EGQL, eliminating the need for gradient information by directly minimizing the Q-function values to derive optimal control strategies. This integration enables precise and robust exploration of the solution space, resulting in accurate and reliable control policies. Furthermore, convergence and monotonic improvement are ensured by the EGQL algorithm, making it suitable for uncertain and nonlinear environments. The effectiveness and superiority of the ANFIS-based EGQL algorithm are validated through simulation results. The developed algorithm achieves a 3.1% reduction in total cost compared to the traditional approach, demonstrating superior control performance.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3035-3046"},"PeriodicalIF":11.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328641","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

Resilient Cyber-Physical Co-Design for Fuzzy Renewable-Integrated Power System Under Frequency-Driven Cyberattacks 频率驱动网络攻击下模糊可再生集成电力系统弹性网络物理协同设计

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-06-19 DOI: 10.1109/tfuzz.2025.3574687

Jia Ding, Jun Wang, Kaibo Shi, Xiao Cai, Oh-Min Kwon, Shiping Wen

引用次数: 0