IEEE Transactions on Fuzzy Systems最新文献_第8页

Progressively Generated Text-Assisted Image Aesthetic Quality Assessment 渐进式生成文本辅助图像审美质量评价

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-05-01 DOI: 10.1109/tfuzz.2025.3566145

Hancheng Zhu, Ju Shi, Zhiwen Shao, Rui Yao, Kunyang Sun, Leida Li

引用次数: 0

An Asymmetric Approach to Three-Way Approximation of Fuzzy Sets 模糊集三向逼近的非对称方法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-30 DOI: 10.1109/TFUZZ.2025.3565700

Xuerong Zhao;Duoqian Miao;Yiyu Yao;Witold Pedrycz

{"title":"An Asymmetric Approach to Three-Way Approximation of Fuzzy Sets","authors":"Xuerong Zhao;Duoqian Miao;Yiyu Yao;Witold Pedrycz","doi":"10.1109/TFUZZ.2025.3565700","DOIUrl":"10.1109/TFUZZ.2025.3565700","url":null,"abstract":"The three-way approximation of fuzzy sets represents membership values using a three-valued set <inline-formula> <tex-math>$lbrace mathbf{1}, mathbf{m}, mathbf{0}rbrace$</tex-math></inline-formula>, where 1 indicates total belongingness, 0 total nonbelongingness, and m an intermediate state. This approach elevates values of membership function above a threshold <inline-formula> <tex-math>$alpha$</tex-math></inline-formula> to 1, reduces those below <inline-formula> <tex-math>$beta$</tex-math></inline-formula> to 0, and assigns the remaining ones to an intermediate value m. A key challenge lies in determining the thresholds <inline-formula> <tex-math>$alpha$</tex-math></inline-formula> and <inline-formula> <tex-math>$beta$</tex-math></inline-formula> and selecting the value of m, as existing models often lack analytical solutions and fail to fully explore the relationship between m and membership structures. This study introduces an asymmetric three-way approximation model for fuzzy sets, removing the constraint <inline-formula> <tex-math>$alpha + beta = 1$</tex-math></inline-formula>. Analytical formulas are derived for the thresholds <inline-formula> <tex-math>$ alpha $</tex-math></inline-formula> and <inline-formula><tex-math>$ beta $</tex-math></inline-formula> by minimizing information loss, and the relationship between m and membership structures is thoroughly examined. An adaptive optimizer is proposed to learn the approximate optimal value of m by minimizing the information loss. The experimental results show that information loss decreases initially before increasing as m grows. Besides, our model achieves the best classification across most datasets.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2348-2360"},"PeriodicalIF":10.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143893134","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 Hierarchical Stochastic Configuration Networks for Industrial Soft Sensor Modeling 工业软传感器建模的模糊分层随机组态网络

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-28 DOI: 10.1109/TFUZZ.2025.3562333

Xinyu Zhou;Jun Lu;Jinliang Ding

{"title":"Fuzzy Hierarchical Stochastic Configuration Networks for Industrial Soft Sensor Modeling","authors":"Xinyu Zhou;Jun Lu;Jinliang Ding","doi":"10.1109/TFUZZ.2025.3562333","DOIUrl":"10.1109/TFUZZ.2025.3562333","url":null,"abstract":"Traditional stochastic configuration networks (SCNs)-based industrial soft sensors have the shortcomings of failing to account for “slowness” characteristic and struggling with processing rule-based information. The original slow feature extraction methods based on autoencoders with fixed structure are lack of flexibility and difficult to maintain a balance between efficiency and accuracy. To address these challenges, a framework of fuzzy hierarchical SCNs (FHSCNs) is proposed, which consists of a slow feature extraction block, a fuzzy inference block and an enhanced output block. The slow feature extraction block is designed, which utilizes a autoencoder based on two SCNs with shared-parameters and the incremental learning paradigm of SCNs to efficiently and adaptively extract the slow-varying latent features. The fuzzy inference block is proposed, which can process rule-based slow feature information. The fuzzy inference block can allow the model to have the fuzzy reasoning capabilities and improve the model interpretability. The enhanced output block with an enhancement layer and a direct-connect portion is presented, which enables the FHSCNs to have the ability of capturing both linearity and nonlinearity of the fuzzy rule-based features. The proposed framework is validated through comprehensive experiments to demonstrate its effectiveness in constructing industrial soft sensor model.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2336-2347"},"PeriodicalIF":10.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884405","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

Misclassification-Error-Inspired Ensemble of Interpretable First-Order TSK Fuzzy Subclassifiers: A Novel Multiview Learning Perspective 可解释一阶TSK模糊子分类器的误分类-错误启发集成：一种新的多视角学习视角

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-23 DOI: 10.1109/TFUZZ.2025.3563636

Maosen Long;Fu-Lai Chung;Shitong Wang

{"title":"Misclassification-Error-Inspired Ensemble of Interpretable First-Order TSK Fuzzy Subclassifiers: A Novel Multiview Learning Perspective","authors":"Maosen Long;Fu-Lai Chung;Shitong Wang","doi":"10.1109/TFUZZ.2025.3563636","DOIUrl":"10.1109/TFUZZ.2025.3563636","url":null,"abstract":"This study explores a novel interpretable Takagi–Sugeno–Kang (TSK) fuzzy ensemble classifier called MEI-TSK from a multiview learning perspective. Unlike most existing fuzzy ensemble classifiers where aggregation learning performs only after the training of multiple fuzzy subclassifiers, MEI-TSK first allows the determination of the antecedents of all fuzzy rules in an individual way for each of TSK fuzzy subclassifiers. It then employs the proposed misclassification-error-inspired learning to accomplish its ensemble learning by training the consequents of all fuzzy rules of each TSK fuzzy subclassifier in a multiview learning way with the simplest averaging aggregation. As a result, the misclassification error caused by such an ensemble learning of fuzzy subclassifiers is theoretically upper bounded. MEI-TSK also features the use of both Bernoulli random feature selection and random feature permutation. The permuted features can be conveniently useful for determining all the antecedents of fuzzy rules with diversity guarantee among all the subclassifiers, and accordingly, be discarded after ensemble learning, resulting in shorter fuzzy rules and improved generalization capability. The experimental results indicate the effectiveness of MEI-TSK in terms of classification performance and/or interpretability.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2321-2335"},"PeriodicalIF":10.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867033","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

Inverse Q-Learning Optimal Control for Takagi–Sugeno Fuzzy Systems Takagi-Sugeno模糊系统的逆q学习最优控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-22 DOI: 10.1109/TFUZZ.2025.3563361

Wenting Song;Jun Ning;Shaocheng Tong

{"title":"Inverse Q-Learning Optimal Control for Takagi–Sugeno Fuzzy Systems","authors":"Wenting Song;Jun Ning;Shaocheng Tong","doi":"10.1109/TFUZZ.2025.3563361","DOIUrl":"10.1109/TFUZZ.2025.3563361","url":null,"abstract":"Inverse reinforcement learning optimal control is under the framework of learner–expert, the learner system can learn expert system's trajectory and optimal control policy via a reinforcement learning algorithm and does not need the predefined cost function, so it can solve optimal control problem effectively. This article develops a fuzzy inverse reinforcement learning optimal control scheme with inverse reinforcement learning algorithm for Takagi–Sugeno (T–S) fuzzy systems with disturbances. Since the controlled fuzzy systems (learner systems) desire to learn or imitate expert system's behavior trajectories, a learner–expert structure is established, where the learner only know the expert system's optimal control policy. To reconstruct expert system's cost function, we develop a model-free inverse Q-learning algorithm that consists of two learning stages: an inner Q-learning iteration loop and an outer inverse optimal iteration loop. The inner loop aims to find fuzzy optimal control policy and the worst-case disturbance input via learner system's cost function by employing zero-sum differential game theory. The outer one is to update learner system's state-penalty weight via only observing expert systems' optimal control policy. The model-free algorithm does not require that the controlled system dynamics are known. It is proved that the designed algorithm is convergent and also the developed inverse reinforcement learning optimal control policy can ensure T–S fuzzy learner system to obtain Nash equilibrium solution. Finally, we apply the presented fuzzy inverse Q-learning optimal control method to nonlinear unmanned surface vehicle system and the computer simulation results verified the effectiveness of the developed scheme.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2308-2320"},"PeriodicalIF":10.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862111","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 Opinion Dynamics-Driven Consensus Model Within Hybrid Mesoscale Structure in Social Trust Network 社会信任网络混合中尺度结构中的模糊意见动态驱动共识模型

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-21 DOI: 10.1109/TFUZZ.2025.3562944

Fei Teng;Xinran Liu;Peide Liu;Xiaoming Wu

{"title":"Fuzzy Opinion Dynamics-Driven Consensus Model Within Hybrid Mesoscale Structure in Social Trust Network","authors":"Fei Teng;Xinran Liu;Peide Liu;Xiaoming Wu","doi":"10.1109/TFUZZ.2025.3562944","DOIUrl":"10.1109/TFUZZ.2025.3562944","url":null,"abstract":"Large-scale group decision making provides an effective way to obtain desirable group decision outcomes. Many studies have neglected the effect of opinion dynamics on the consensus reaching process (CRP) in the social trust network (STN) with a hybrid mesoscale structure. In addition, the application of communication theory to the CRP has rarely been considered. This article is devoted to building a fuzzy opinion dynamics-driven consensus model through the detection of hybrid mesoscale structures. First, this article proposes a novel mesoscale structure detection method that can detect both overlapping community structures and core–periphery structures in the STN. Then, to capture the opinion dynamics process under hybrid mesoscale structures, this article improves the opinion dynamics model by delineating five categories of nodes. Considering the dynamic changes of the CRP, a consensus feedback mechanism driven by opinion dynamics is proposed. In addition, the opinion adjustment is based on the “Spiral of Silence Theory.” Finally, the practicality of the proposed model is illustrated through an example. The superiority of the new detection algorithm and the efficiency of the novel consensus model are demonstrated through comparative analysis.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2295-2307"},"PeriodicalIF":10.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858054","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 Jointly Sparse Fast Fuzzy Clustering via Ternary-Tree-Based Anchor Graph 通过基于三元树的锚图进行稳健的联合稀疏快速模糊聚类

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-18 DOI: 10.1109/TFUZZ.2025.3562384

Jianping Liu;Hongying Zhang;Kezhen Dong;Feiping Nie

{"title":"Robust Jointly Sparse Fast Fuzzy Clustering via Ternary-Tree-Based Anchor Graph","authors":"Jianping Liu;Hongying Zhang;Kezhen Dong;Feiping Nie","doi":"10.1109/TFUZZ.2025.3562384","DOIUrl":"10.1109/TFUZZ.2025.3562384","url":null,"abstract":"Traditional partition-based fuzzy clustering algorithms are widely used for revealing possible hidden structures in data. However, high computational cost limits their applications in large-scale and high-dimensional data. Moreover, most fuzzy clustering algorithms are sensitive to noise. To tackle these issues, a robust jointly sparse fast fuzzy clustering algorithm via anchor graph (RSFCAG) is proposed and analyzed in this article. Specifically, we first propose a fast k-means method integrated shadowed set and balanced ternary tree, which serves as a fast hierarchical clustering approach by partitioning every cluster into three subclusters at each layer (3KHK). 3KHK can quickly obtain the anchor set and its optimization is solved fast by the simplex method, which also captures the ambiguity and uncertainty between clusters in large-scale clustering tasks. Second, a similarity matrix learning approach based on possibilistic neighbors is further proposed to get a robust similarity graph, which strengthens the ability of fuzzy clustering to handle large-scale data. Furthermore, the orthogonal projection matrix is integrated into the RSFCAG framework to transform the original high-dimensional space into low-dimensional space. Finally, the <inline-formula><tex-math>$L_{2,1}$</tex-math></inline-formula>-norm loss and regularization are integrated into the joint algorithm RSFCAG, which is solved optimally by block coordinate technique, to enhance the robustness and interpretability of the fuzzy clustering process. The experimental results demonstrate the effectiveness and efficiency of our proposed method in most of benchmark datasets.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2284-2294"},"PeriodicalIF":10.7,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849799","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 Control Analysis With Model Transformation for Interval Type-2 Fuzzy Systems 区间2型模糊系统的模型变换鲁棒控制分析

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-16 DOI: 10.1109/TFUZZ.2025.3561333

Jie Yang;Shaoyan Gai;Feipeng Da;Wenbo Xie

{"title":"Robust Control Analysis With Model Transformation for Interval Type-2 Fuzzy Systems","authors":"Jie Yang;Shaoyan Gai;Feipeng Da;Wenbo Xie","doi":"10.1109/TFUZZ.2025.3561333","DOIUrl":"10.1109/TFUZZ.2025.3561333","url":null,"abstract":"To address the problem of robust stability analysis for interval type-2 fuzzy systems (IT2FSs), this article proposes an innovative analysis approach based on model transformation. First, a classical piecewise linear approximation method is utilized to process the upper boundary membership functions and lower boundary membership functions of the footprint of uncertainty in IT2FSs, resulting in linear boundary membership functions (MFs) that are more convenient for analysis, along with the corresponding approximation error functions. Subsequently, a novel error model transformation method is introduced to handle these error terms. By constructing new fuzzy rules and MFs, the boundary error terms are converted into a new fuzzy model, thereby incorporating more information about the error functions into the stability analysis. Based on this model, a robust stability condition in the form of linear matrix inequalities are derived, achieving improved robustness. Finally, the effectiveness of the proposed method is validated through simulations on real-world systems, and its superiority is demonstrated by comparison with existing methods.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2272-2283"},"PeriodicalIF":10.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841849","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 Fixed-Time Control for Uncertain Time-Delay Nonlinear Systems With Output Constraints 具有输出约束的不确定时延非线性系统的自适应模糊定时控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-14 DOI: 10.1109/TFUZZ.2025.3560644

Yang Bai;Gang Dou;Tianliang Zhang;Weihai Zhang;Mei Guo

引用次数: 0

Incorporating Large Vision Model Distillation and Fuzzy Perception for Improving Disease Diagnosis 结合大型视觉模型蒸馏和模糊感知改进疾病诊断

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-09 DOI: 10.1109/tfuzz.2025.3559562

Zhan Gao, Qika Lin, Huaxuan Wen, Bin Pu, Mengling Feng, Kenli Li

引用次数: 0