IEEE Transactions on Fuzzy Systems最新文献_第3页

Detection and Defense-Based Fixed-Time Output Synchronization of Fuzzy Stochastic Multilayered Networks Under Deception Attacks 欺骗攻击下基于检测与防御的模糊随机多层网络固定时间输出同步

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-10-07 DOI: 10.1109/TFUZZ.2026.3720451

Yuhua Gao;Cheng Hu;Juan Yu;Shiping Wen;Quanxin Zhu

{"title":"Detection and Defense-Based Fixed-Time Output Synchronization of Fuzzy Stochastic Multilayered Networks Under Deception Attacks","authors":"Yuhua Gao;Cheng Hu;Juan Yu;Shiping Wen;Quanxin Zhu","doi":"10.1109/TFUZZ.2026.3720451","DOIUrl":"https://doi.org/10.1109/TFUZZ.2026.3720451","url":null,"abstract":"The coexistence of stochastic disturbances and cyber-attacks presents fundamental challenges to traditional deterministic fixed-time stability theory, hindering rigorous solutions to fixed-time synchronization of stochastic complex networks in adversarial environments. To address this problem, this article investigates fixed-time output synchronization in probability for fuzzy stochastic multilayered networks under deception attacks. First, a relaxed fixed-time convergence condition is established for stochastic systems, providing refined parameter constraints and accurate convergence time estimates. Based on these stability results, a concise power-law-based controller is designed for attack-free networks to achieve stochastic fixed-time synchronization. For networks subject to deception attacks, a discontinuous controller is proposed along with an <inline-formula><tex-math>$L_{2}$</tex-math></inline-formula>-norm Lyapunov function. Furthermore, a novel detection mechanism based on likelihood-ratio and its corresponding active defense control algorithm are developed to accurately identify anomalous system dynamics under attack interference. Finally, the theoretical results are validated through numerical simulations based on a traffic network model.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3447-3460"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097620","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

MGE-DNTSK: A Multi-Granularity Ellipsoidal Dual-Neural TSK Fuzzy Classification System MGE-DNTSK：一个多粒度椭球体双神经模糊分类系统

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-08-11 DOI: 10.1109/TFUZZ.2026.3722546

Zhen-Sheng Zang;Meng-Wei Li;Wei Lu;Li-Yong Zhang;Witold Pedrycz

{"title":"MGE-DNTSK: A Multi-Granularity Ellipsoidal Dual-Neural TSK Fuzzy Classification System","authors":"Zhen-Sheng Zang;Meng-Wei Li;Wei Lu;Li-Yong Zhang;Witold Pedrycz","doi":"10.1109/TFUZZ.2026.3722546","DOIUrl":"10.1109/TFUZZ.2026.3722546","url":null,"abstract":"Traditional Takagi–Sugeno–Kang (TSK) fuzzy systems suffer from three fundamental limitations: Exponential rule growth from Cartesian product-based generation, cross-dimensional interaction constraints imposed by T-norm aggregation, and limited expressiveness of linear consequents. To simultaneously address these shortcomings, this article proposes Multi-Granularity Ellipsoidal Dual-Neural TSK (MGE-DNTSK), a novel neural fuzzy classification system comprising a hierarchical granulation layer and a dual-neural TSK (DNTSK) transformation layer. Unlike existing approaches that tackle these limitations in isolation, MGE-DNTSK unifies differentiable information granulation with neural rule generation within a single end-to-end framework. Specifically, the granulation layer constructs tilted hyper-ellipsoidal granules via low-rank Cholesky factorization across coarse, medium, and fine tiers, encoding inter-feature correlations into membership representations with linear parameter complexity. Building upon these representations, the DNTSK layer replaces Cartesian product-based rule generation and linear consequent functions with a shared neural antecedent incorporating adaptive pruning and independent neural consequents, thereby decoupling rule complexity from input dimensionality while enhancing local nonlinear expressiveness. The entire architecture is optimized through a three-stage curriculum protocol. Experiments on 16 benchmark datasets show that MGE-DNTSK achieves leading performance across four evaluation metrics against 15 representative baselines, particularly on high-dimensional data, and a real-world industrial fault-diagnosis case study further exhibits its practical effectiveness.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3389-3403"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728143","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

Decentralized Fuzzy Adaptive Control for Continuous Rolling Mill Trains: A Mixed-Order Fully Actuated System Approach 连轧机组的分散模糊自适应控制：一种混合阶全驱动系统方法

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-08-19 DOI: 10.1109/TFUZZ.2026.3725480

Peng Wang;Minrui Fei;Dajun Du;Cheng Qian;Yangjie Chen

{"title":"Decentralized Fuzzy Adaptive Control for Continuous Rolling Mill Trains: A Mixed-Order Fully Actuated System Approach","authors":"Peng Wang;Minrui Fei;Dajun Du;Cheng Qian;Yangjie Chen","doi":"10.1109/TFUZZ.2026.3725480","DOIUrl":"10.1109/TFUZZ.2026.3725480","url":null,"abstract":"This article proposes a decentralized fuzzy adaptive control strategy based on the fully actuated system approach to address vertical–torsional coupled vibration (VTCV) and interstand tension coupling effects in continuous rolling mill trains during high-speed rolling. First, a mixed-order fully actuated interconnected system model is developed that integrates the vertical–torsional dynamics of individual stands with interstand tension transmission, thereby incorporating nonlinear vibrations, tension coupling, and thickness–speed–tension chain interactions into a unified modeling framework. Next, a decentralized fuzzy adaptive controller is designed using a mixed-order fully actuated system approach to achieve effective VTCV suppression and coordinated interstand tension stabilization. A speed-error constraint mechanism is embedded to ensure roll speed matching across multiple stands and to maintain strip surface flatness. Lyapunov stability analysis rigorously proves that the principal tracking errors asymptotically converge to zero, while all adaptive parameter estimates and control inputs remain uniformly bounded, thereby ensuring that vibrations are suppressed and tension coupling effects are accurately compensated. Finally, a simulation model of a 1450-<inline-formula><tex-math>$, text{mm}$</tex-math></inline-formula> rolling mill is built on the MATLAB/Simulink platform incorporating comparative simulations to verify the robustness and engineering applicability of the proposed strategy under complex operating conditions.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3419-3431"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148768171","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

Differentiable Softmin-Driven Self-Organizing Fuzzy ARTMAP via Unified End-to-End Learning 基于统一端到端学习的可微软驱动自组织模糊ARTMAP

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-07-08 DOI: 10.1109/TFUZZ.2026.3710814

Peng Zhou;Zheng Wang;Eun-Hu Kim;Zunwei Fu;Qingyan Wu;Witold Pedrycz

{"title":"Differentiable Softmin-Driven Self-Organizing Fuzzy ARTMAP via Unified End-to-End Learning","authors":"Peng Zhou;Zheng Wang;Eun-Hu Kim;Zunwei Fu;Qingyan Wu;Witold Pedrycz","doi":"10.1109/TFUZZ.2026.3710814","DOIUrl":"10.1109/TFUZZ.2026.3710814","url":null,"abstract":"Fuzzy neural networks and rule-based models offer excellent interpretability, but scaling them for complex, high-dimensional problems remains a challenging task. Most conventional prototype-based models rely on nondifferentiable operations and decoupled learning strategies. While recent advances enable fully differentiable training, they typically operate within rigid, predefined topologies frozen by manual partitioning or offline clustering, thereby forfeiting dynamic structural adaptability. Here, we primarily focus on addressing the bottlenecks of nondifferentiability and rigid topological constraints in designing modern fuzzy systems. We propose the differentiable Softmin-driven self-organizing Fuzzy ARTMAP (DS-SOFAM). The main novelty is the introduction of a continuous relaxation via a temperature-controlled Softmin operator to replace hard discrete matching, which allows DS-SOFAM to seamlessly integrate dynamic, self-organizing prototype generation into a fully differentiable framework, enabling joint parameter optimization via the Adam algorithm. The proposed model is tested on 29 benchmark datasets, including classical and high-dimensional face recognition tasks. Our findings demonstrate that DS-SOFAM achieves competitive or superior classification accuracy while maintaining a significantly lower structural footprint and preserving transparent rule interpretability.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3127-3142"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414788","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

Game-Based Fully Distributed Optimal Fuzzy Encircling Control for Multiagent Systems 基于博弈的多智能体系统全分布最优模糊环控制

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-07-24 DOI: 10.1109/TFUZZ.2026.3716818

Yongming Li;Mingshuo Wang;Kewen Li

{"title":"Game-Based Fully Distributed Optimal Fuzzy Encircling Control for Multiagent Systems","authors":"Yongming Li;Mingshuo Wang;Kewen Li","doi":"10.1109/TFUZZ.2026.3716818","DOIUrl":"10.1109/TFUZZ.2026.3716818","url":null,"abstract":"This article investigates the fully distributed optimal encircling control problem for uncertain nonlinear multiagent systems, which incorporate a Nash equilibrium game among individual agents and a zero-sum game between agents and the target. First, a fully distributed observer is designed, enabling each agent to estimate the target states without requiring global states and topological information. The observer gain can be adaptively adjusted in accordance with the estimation error, and finally converges to a fixed value. Then, the encircling control task can be decomposed into two subtasks, that is, distance control task and phase control task. By constructing the game-theoretic cost function and using gradient descent algorithm, the Nash equilibrium solution can be solved. Subsequently, a reinforcement learning-based backstepping method is used, which considers the zero-sum game between the target with adversarial dynamics and the agent. In each step, computationally efficient single critic fuzzy logic systems are used to approximate the unknown nonlinear dynamics, and the optimal control policy is approximately obtained through policy iteration method. Theoretical analysis proves that the proposed optimal control method effectively addresses the fully distributed noncooperative encircling problem. Simulation results further verify the effectiveness of the proposed control scheme.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3182-3195"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586827","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

Dual Fuzzy Learning With Global–Local Structure Fusion for Scalable Multiview Subspace Clustering 基于全局-局部结构融合的双模糊学习可扩展多视图子空间聚类

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-08-03 DOI: 10.1109/TFUZZ.2026.3719791

Taiyong Li;Wei Fan

{"title":"Dual Fuzzy Learning With Global–Local Structure Fusion for Scalable Multiview Subspace Clustering","authors":"Taiyong Li;Wei Fan","doi":"10.1109/TFUZZ.2026.3719791","DOIUrl":"10.1109/TFUZZ.2026.3719791","url":null,"abstract":"Anchor guidance mechanism has demonstrated strong scalability for clustering large-scale datasets, driving its widespread applications in multiview clustering in recent years. Accordingly, a series of anchor-based multiview subspace clustering methods have been proposed to improve computational efficiency. However, many of these methods suffer from three major limitations. First, they generally focus on the integrated or specific local structures, while ignoring global information. Second, the typical postprocessing with singular value decomposition and <inline-formula><tex-math>$k$</tex-math></inline-formula>-means incurs extra time burden and yields volatile clustering performance. Third, different views may contain noise and exhibit complex relationships, making crisp partitioning unreliable and thereby requiring robust, uncertainty-aware learning. To address these issues, a scalable dual fuzzy learning and structure fusion multiview subspace clustering (DFFMSC) framework is proposed. Inspired by the effectiveness of fuzzy clustering in handling uncertain data, we incorporate fuzzy learning to enable more flexible and robust cluster partitioning. Unlike many multiview fuzzy clustering methods involving intricate cooperative learning terms, we incorporate two concise fuzzy learning terms into a dual-facilitated fuzzy learning component. One integrates the separate compact representations to learn a unified fuzzy membership matrix, while the other simultaneously enhances it through global–local structure fusion learning. With a fast alternating optimization algorithm, the labels are directly obtained from the optimal fuzzy membership matrix. Overall, the proposed DFFMSC not only adequately fuses the local and global structures, but also achieves one-step clustering without postprocessing. Extensive experiments on nine popular multiview datasets validate its effectiveness and efficiency.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3304-3318"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148682291","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

Transfer Learning With Fuzzy Elevation-Aware Experts for Bathymetric DEM Super-Resolution 基于模糊高程感知专家的高程DEM超分辨率迁移学习

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-08-10 DOI: 10.1109/TFUZZ.2026.3720457

Chaofan Yang;Mingjun Yang;Chenhao Wu;Mengyue Sun;Xiaomeng Huang;Meng Zhang;Bo Zhang;Chenhu Huang;Haifeng Zhang

{"title":"Transfer Learning With Fuzzy Elevation-Aware Experts for Bathymetric DEM Super-Resolution","authors":"Chaofan Yang;Mingjun Yang;Chenhao Wu;Mengyue Sun;Xiaomeng Huang;Meng Zhang;Bo Zhang;Chenhu Huang;Haifeng Zhang","doi":"10.1109/TFUZZ.2026.3720457","DOIUrl":"10.1109/TFUZZ.2026.3720457","url":null,"abstract":"High-resolution land–ocean integrated digital elevation models (DEMs) are critical for navigation, coastal management, and marine engineering in strait environments. However, existing DEM products are too coarse, while bathymetric observations are sparse, heterogeneous, and unevenly distributed, making high-resolution reconstruction difficult. To address this problem, this study proposes a transfer learning-based fuzzy elevation-aware mixture of experts (TFEA-MoE) framework for DEM super-resolution in the Taiwan Strait. The core methodological contribution is an elevation-aware fuzzy expert routing mechanism, which uses learnable Gaussian membership functions to softly assign pixels to shallow, transition, and deep-water experts. Embedded in a Bass-Strait-to-Taiwan-Strait transfer-learning framework, this routing strategy enables smooth and physically interpretable residual correction under sparse ATL24 and shipborne supervision. Experimental results show that TFEA-MoE achieves the lowest errors among the compared methods under the evaluated protocols. Based on held-out sparse bathymetric observations, compared with the average performance of all baseline methods, TFEA-MoE reduces root mean square error (RMSE), mean absolute error (MAE), slope RMSE, and slope MAE by 23.87%, 34.01%, 22.02%, and 21.48%, respectively. Ablation studies confirm the contributions of transfer learning and fuzzy expert routing, with fuzzy refinement reducing the four metrics by 12.36%, 34.89%, 12.26%, and 16.05%, respectively. Because these observation-level improvements may be partially influenced by spatial autocorrelation, spatially independent testing is used as the main evidence for target-domain generalization, with uncertainty and seam-continuity analyses supporting reconstruction stability. These results indicate that TFEA-MoE provides a promising and interpretable approach for DEM super-resolution under sparse and heterogeneous bathymetric observations.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3374-3388"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728149","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 Digital Twin-Guided Deep Evidential Framework for Real-Time UAV Risk Assessment With Fuzzy Risk Semantics 基于模糊风险语义的无人机实时风险评估数字双导深度证据框架

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-07-31 DOI: 10.1109/TFUZZ.2026.3717993

Jinhu Tu;Xiaohong Nian;Xunhua Dai

{"title":"A Digital Twin-Guided Deep Evidential Framework for Real-Time UAV Risk Assessment With Fuzzy Risk Semantics","authors":"Jinhu Tu;Xiaohong Nian;Xunhua Dai","doi":"10.1109/TFUZZ.2026.3717993","DOIUrl":"10.1109/TFUZZ.2026.3717993","url":null,"abstract":"Reliable, real-time risk assessment is essential for autonomous unmanned aerial vehicles (UAVs) operating in complex, evolving missions. Existing approaches, relying on discrete models, offline analysis, or fixed assumptions, struggle to capture fuzzy risk semantics and predictive uncertainty, leading to degraded performance under unseen flight envelopes. To address this, we propose a digital twin (DT)-driven online framework integrating fuzzy semantic modeling with deep evidential learning. A dynamically consistent DT provides an evolving healthy baseline, enabling risk modeling in relative deviation space and reducing dependence on specific operating conditions. Risk levels are encoded as interval-valued fuzzy sets, while deep evidential learning jointly infers risk probabilities and uncertainty, allowing explicit confidence evaluation under novel scenarios. Extensive system-level experiments across multiple missions, fault types, and unseen envelopes demonstrate stable, continuous, and uncertainty-aware performance, substantially improving real-time reliability and generalization. This framework shifts risk assessment from outcome-based classification to process-level understanding, establishing a foundation for interpretable safety mechanisms in autonomous UAVs. Source code and a multiscenario UAV risk dataset are released online to support future research.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3276-3290"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148633939","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

FNP-Miner: Fuzzy Three-Way Nonoverlapping Sequential Pattern Mining FNP-Miner：模糊三向非重叠顺序模式挖掘

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-08-06 DOI: 10.1109/TFUZZ.2026.3721254

Youxi Wu;Yunda Qiao;Jing Liu;Yan Li;Cong Liu;Jianguo Wei;Wenjian Wang

{"title":"FNP-Miner: Fuzzy Three-Way Nonoverlapping Sequential Pattern Mining","authors":"Youxi Wu;Yunda Qiao;Jing Liu;Yan Li;Cong Liu;Jianguo Wei;Wenjian Wang","doi":"10.1109/TFUZZ.2026.3721254","DOIUrl":"https://doi.org/10.1109/TFUZZ.2026.3721254","url":null,"abstract":"In sequential pattern mining (SPM), three-way SPM aims to reduce redundant patterns by classifying items into weak, medium, and strong groups, mining only patterns of medium and strong items with no strong items in pattern gaps. However, this approach requires users to categorize the items manually, producing discrete results with undetermined boundaries, and its rigid gap constraint overfilters valid patterns. To address this problem, based on item frequency and user interest, we mine fuzzy three-way nonoverlapping sequential patterns (FNPs) with flexible quantitative gap constraints, and propose an algorithm called FNP-Miner. Fuzzy three-way nonoverlapping sequential pattern (FNP) mining does not satisfy the antimonotonicity property, and we overcome this limitation by deriving a fuzzy support upper bound and designing a pruning strategy to effectively reduce the number of candidate patterns. Furthermore, to enhance the efficiency of fuzzy support computation, the support of a pattern is calculated using the occurrence information of its subpatterns based on an inverted index structure. To evaluate the performance of FNP-Miner, we compare it with 13 competitive algorithms on eight benchmark datasets, and propose two indicators to measure the interest degree of patterns and gaps. The experimental results show that FNP-Miner finds patterns that are more preferred by the user, and achieves better performance than the compared algorithms.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"34 10","pages":"3334-3348"},"PeriodicalIF":10.2,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097247","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

IEEE Transactions on Fuzzy Systems Publication Information IEEE模糊系统学报

IF 10.2 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2026-10-01 Epub Date: 2026-10-07 DOI: 10.1109/TFUZZ.2026.3731883

引用次数: 0