IEEE Transactions on Fuzzy Systems最新文献

{"title":"$H_{-}/L_{infty }$ Time-delay Unknown Input Actuator Fault Detection Observer for Discrete-time Takagi-Sugeno Fuzzy Singular Systems and Zonotopic Residual Analysis","authors":"Jiafeihong Chen, Zhiguang Feng, Hak-Keung Lam, Ligang Wu","doi":"10.1109/tfuzz.2025.3608029","DOIUrl":"https://doi.org/10.1109/tfuzz.2025.3608029","url":null,"abstract":"","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"15 1","pages":""},"PeriodicalIF":11.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035503","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}

{"title":"Robust Classification via Interval Type-2 Fuzzy C-Means and Gradient Boosting","authors":"Yunlong Zhu;Haibin Duan;Zheng Wang;Eun-Hu Kim;Zunwei Fu;Witold Pedrycz","doi":"10.1109/TFUZZ.2025.3583051","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3583051","url":null,"abstract":"This article introduces the Bayesian probabilistic fuzzy neural network (BPFNN), designed to overcome the limitations of Fuzzy C-Means (FCM) clustering, which struggles with uncertainty, noise, nonlinearity, and interpretability. Additionally, it addresses the shortcomings of traditional objective functions, such as mean squared error (MSE), which fail to capture the complexities inherent in high-dimensional and uncertain datasets. The BPFNN framework integrates Bayesian probabilistic modeling with advanced fuzzy clustering techniques, utilizing a non-Gaussian probability density function to better represent data uncertainties. A hybrid Markov chain Monte Carlo strategy, combining Metropolis-Hastings for membership updates and Gibbs sampling for cluster parameter estimation, is employed to effectively model uncertainty. For the learning of connection weights, the generalized cross-entropy loss function is applied, and the iteratively reweighted least squares algorithm is used to update the weights, allowing for a more precise quantification of the divergence between predicted and ground truth labels. Experimental evaluations on several benchmark datasets, as well as a high-dimensional laser-induced breakdown spectroscopy (LIBS) spectral dataset, demonstrate that the proposed BPFNN significantly outperforms both traditional methods and State-of-the-Art techniques in terms of classification accuracy and robustness. Notably, BPFNN achieves an average accuracy improvement of 3.2% over conventional models on benchmark datasets, with a 5.3% improvement on the LIBS dataset, highlighting its substantial advancement in the field.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3103-3117"},"PeriodicalIF":11.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998147","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}

{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TFUZZ.2025.3597494","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3597494","url":null,"abstract":"","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"C3-C3"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934423","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}

{"title":"Group Consensus Formation Under Fuzzy Preferences: Addressing Hypocritical Trust and Irrational Confidence","authors":"Yu Wang;Jianming Zhan","doi":"10.1109/TFUZZ.2025.3605876","DOIUrl":"10.1109/TFUZZ.2025.3605876","url":null,"abstract":"With the rapid development of artificial intelligence (AI), group decision-making (GDM) has become a critical approach to ensuring the scientificity and fairness of decisions in complex systems. However, existing GDM methods often face limitations due to the presence of hypocritical trust and irrational confidence among decision-makers (DMs), which can significantly hinder the effective achievement of consensus. To address these challenges, this article proposes an innovative consensus reaching process that integrates fuzzy preference relations and advanced AI techniques to systematically identify and eliminate irrational factors in the decision-making process. The proposed method introduces a novel mechanism for quantifying the contribution degree of DMs based on game theory within the framework of fuzzy sociometric relations and effectively identifies and eliminates hypocritical trust relationships using geometric analysis. In addition, an innovative quartile classification method based on dynamic monitoring is designed to achieve real-time assessment and adjustment of the confidence level of DMs. By combining principles of calculus and geometry, an accurate calculation model of DM weights is constructed. The proposed method establishes a two-level consensus feedback mechanism integrating confidence level and trust degree, ultimately realizing the selection of the optimal alternative. Systematic case studies and comparative experimental analyses demonstrate the significant superiority of the proposed method in terms of consensus efficiency and decision-making quality. The key innovations of this research include the development of a new weight determination method based on calculus and geometry, the construction of a hypocritical trust identification mechanism using game theory and geometric analysis, and the establishment of an irrational confidence regulation system combining regret theory with dynamic monitoring. These contributions provide a robust theoretical framework and methodological support for addressing consensus challenges in complex GDM scenarios.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3808-3822"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144987629","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}

{"title":"Adaptive Fuzzy Tracking Control With Quantized Prescribed Performance for Nonlinear Systems Under Unknown Disturbances","authors":"Xin Liu;Jiayue Sun;Ying Yan;Shiyang Wang","doi":"10.1109/TFUZZ.2024.3456851","DOIUrl":"https://doi.org/10.1109/TFUZZ.2024.3456851","url":null,"abstract":"In this article, the investigation is centered on the problem of fixed-time control within nonlinear systems. A quantized fixed-time prescribed performance control is proposed for the first time, which does not rely on preset thresholds within the performance function. Leveraging the properties of the designed continuous quantizers, the constraint boundaries are dynamically adjusted to ensure that the behavior of the controlled entity remains within a desired narrow range. The challenge of intensive differential computations is bypassed through the adoption of a dynamic surface approach in the backstepping design phase. By seamlessly integrating the principles of backstepping with fuzzy logic systems, an innovative practical fixed-time tracking controller is suggested to address the identified issue. Through a series of simulation examples, the practicality and efficiency of the proposed approach are validated.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"2962-2971"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934427","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}

{"title":"Scalable Fuzzy Clustering With Collaborative Structure Learning and Preservation","authors":"Bingbing Jiang;Chenglong Zhang;Zhongli Wang;Xinyan Liang;Peng Zhou;Liang Du;Qinghua Zhang;Weiping Ding;Yi Liu","doi":"10.1109/TFUZZ.2025.3581679","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3581679","url":null,"abstract":"To partition samples into distinct clusters, Fuzzy C-Means (FCM) calculates the membership degrees of samples to cluster centers and provides soft labels, gaining significant attention in recent years. However, existing FCM methods encounter the following challenges. First, traditional FCM focuses on learning membership degrees, neglecting the data similarity structures. Second, graph-based FCM typically separates graph construction from clustering, overlooking the knowledge interaction between graphs and clustering, obtaining suboptimal performance. Third, exploring the similarity structures among all samples is computationally expensive for large-scale tasks. To solve these dilemmas, we propose a scalable fuzzy clustering with collaborative structure learning and preservation (CSLP), which simultaneously leverages both cluster information and similarity structures to learn an optimal membership degree representation. Specifically, a self-weighted manner is devised to measure the sample importance, thereby reducing the adverse impacts of outliers. Moreover, the graph is updated according to the data similarities in the membership degree representation, such that CSLP collaboratively learns the graph and membership degrees in a mutually reinforcing manner. Thus, the similarity structures are fully explored during clustering processes and preserved in the learned membership degrees, enhancing the discrimination of clustering labels. To further improve efficiency, an acceleration solution is developed to reduce the computational cost of CSLP by propagating membership degrees from potential centers to samples, making CSLP scalable for large-scale tasks. An iterative strategy is designed to solve the formulated objective function. Extensive experiments demonstrate that CSLP outperforms other fuzzy clustering methods in terms of both effectiveness and scalability.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3047-3060"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934481","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}

{"title":"A Novel Relative Density and Nonmembership-Based Intuitionistic Fuzzy Twin SVM for Class Imbalance Learning","authors":"Yash Arora;S. K. Gupta;Shuaiyong Li","doi":"10.1109/TFUZZ.2025.3605436","DOIUrl":"10.1109/TFUZZ.2025.3605436","url":null,"abstract":"A major challenge in machine learning is the accurate categorization of data in the presence of noise, outliers, and imbalanced class distributions. Fuzzy support vector machines and their variants have demonstrated potential in handling noise and outliers but struggle to address the challenge of imbalanced datasets. To deal with this problem, in this article, we propose a novel relative density and nonmembership (RDNM)-based intuitionistic fuzzy twin support vector machine for class imbalance learning. The method utilizes a <inline-formula><tex-math>$k$</tex-math></inline-formula>-nearest neighbor-based probability density estimation to assess the significance of each training pattern. Furthermore, a novel RDNM-based membership function is employed to effectively distinguish noise and outliers from support vectors. To address the class imbalance problem, majority class training patterns are assigned a score function incorporating the imbalance ratio, while minority class patterns are given a higher membership degree of unity. To validate the superiority of the proposed method, comprehensive experiments and statistical analyses are conducted over 30 imbalanced benchmark datasets, employing linear and nonlinear kernels, and the outcomes are compared with the existing approaches. In addition, the proposed model is applied to the HAM10000 dataset, which consists of dermatoscopic images for skin lesion classification, showcasing its effectiveness in medical applications. The experimental results demonstrate that the proposed model surpasses the baseline models, underscoring its capability for addressing classification challenges in real-world applications with imbalanced class distributions.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 10","pages":"3795-3807"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144987631","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}

{"title":"Guest Editorial: Special Issue on Fuzzy Affective Computing Systems","authors":"Sicheng Zhao;Hongxun Yao;Xinde Li;James Z. Wang;Björn W. Schuller","doi":"10.1109/TFUZZ.2025.3595652","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3595652","url":null,"abstract":"","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"2882-2883"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150427","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934472","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}

{"title":"Fuzzy Kernel Stochastic Configuration Networks for Industrial Data Modeling","authors":"Yongxuan Chen;Dianhui Wang","doi":"10.1109/TFUZZ.2025.3580614","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3580614","url":null,"abstract":"Kernel stochastic configuration networks (KSCNs) belong to the randomized learner model with universal approximation property. However, the original KSCNs model lacks logical reasoning ability and the utilization of prior knowledge. This article proposes a novel neurofuzzy learner model based on KSCNs, termed fuzzy KSCNs (F-KSCNs), for enhancing model’s interpretability and modeling performance. First, the Takagi–Sugeno–Kang fuzzy inference system is integrated into the modeling process of KSCNs, where multiple submodels are constructed according to the associated fuzzy rules. Then, the parameters of F-KSCNs are determined by the kernel stochastic configuration algorithm to guarantee model’s universal approximation property. Finally, the output of F-KSCNs is obtained by comprehensively evaluating the significance of each submodel. Compared to the original KSCNs, the proposed F-KSCNs have stronger fuzzy inference ability and can deal with rule-based information. Moreover, computational efficiency is greatly improved under this fuzzy inference framework. Two industrial case studies are carried out for performance evaluation. The experimental results with comparisons with existing solutions clearly show the superiorities of our proposed method, including generalization performance and modeling efficiency.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3001-3011"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934450","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}

{"title":"IEEE Transactions on Fuzzy Systems Publication Information","authors":"","doi":"10.1109/TFUZZ.2025.3597490","DOIUrl":"https://doi.org/10.1109/TFUZZ.2025.3597490","url":null,"abstract":"","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"C2-C2"},"PeriodicalIF":11.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934533","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}