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

Label-Informed Outlier Detection Based on Granule Density 基于颗粒密度的标签通知离群点检测

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-20 DOI: 10.1109/TFUZZ.2024.3514853

Baiyang Chen;Zhong Yuan;Dezhong Peng;Hongmei Chen;Xiaomin Song;Huiming Zheng

{"title":"Label-Informed Outlier Detection Based on Granule Density","authors":"Baiyang Chen;Zhong Yuan;Dezhong Peng;Hongmei Chen;Xiaomin Song;Huiming Zheng","doi":"10.1109/TFUZZ.2024.3514853","DOIUrl":"10.1109/TFUZZ.2024.3514853","url":null,"abstract":"Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semisupervised methods typically treat data as purely numerical and in a deterministic manner, thereby neglecting the heterogeneity and uncertainty inherent in complex, real-world datasets. This article introduces a label-informed outlier detection method for heterogeneous data based on Granular Computing and Fuzzy Sets, namely Granule Density-based Outlier Factor (GDOF). Specifically, GDOF first employs label-informed fuzzy granulation to effectively represent various data types and develops granule density for precise density estimation. Subsequently, granule densities from individual attributes are integrated for outlier scoring by assessing attribute relevance with a limited number of labeled outliers. Experimental results on various real-world datasets show that GDOF stands out in detecting outliers in heterogeneous data with a minimal number of labeled outliers. The integration of Fuzzy Sets and Granular Computing in GDOF offers a practical framework for outlier detection in complex and diverse data types.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1391-1401"},"PeriodicalIF":10.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672349","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

Interval Observer-Based Control of Takagi–Sugeno Fuzzy Systems With Uncertainties 基于区间观测器的不确定Takagi-Sugeno模糊系统控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-20 DOI: 10.1109/TFUZZ.2025.3553300

Ming Chi;Ankang Zhang;Xiaoling Wang;Lintao Ye

引用次数: 0

Predefined-Time Fuzzy Adaptive Optimal Secure Consensus Control for Multiagent Systems With Unknown Follower Dynamics 具有未知Follower动态的多智能体系统的预定义时间模糊自适应最优安全一致性控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-17 DOI: 10.1109/TFUZZ.2025.3552050

Haitao Wang;Qingshan Liu;Ju H. Park

{"title":"Predefined-Time Fuzzy Adaptive Optimal Secure Consensus Control for Multiagent Systems With Unknown Follower Dynamics","authors":"Haitao Wang;Qingshan Liu;Ju H. Park","doi":"10.1109/TFUZZ.2025.3552050","DOIUrl":"10.1109/TFUZZ.2025.3552050","url":null,"abstract":"This article investigates the secure leader–following consensus of multiagent systems with unknown follower dynamics under two types of denial-of-service attacks: connectivity-maintained and connectivity-broken attacks. A predefined-time resilient distributed observer to achieve predefined-time observation of the leader's state is constructed by incorporating a time-varying piecewise function with an online update at the initial instant. During the connectivity-maintained intervals, all agents can achieve predefined-time observation of the leader. During the connectivity-broken intervals, agents that have direct or indirect access to the leader's state can still accomplish the predefined-time observation. Since the leader state observation cannot be ensured for all agents during the connectivity-broken intervals, a new persistent dwell-time switching model is introduced to describe the switching between the two types of attacks, and novel sufficient conditions are derived to ensure that all agents can observe the leader's state based on multiple Lyapunov function approach. Furthermore, a predefined-time sliding mode controller combining the resilient distributed observer and fuzzy reinforcement learning strategy is proposed to ensure leader–following consensus. Finally, the effectiveness of the proposed method is validated through simulations and comparisons using a distributed multiple robot arm system.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2122-2135"},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640790","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 Distributed Adaptive Fuzzy Resilient Nash Equilibrium Seeking for Uncertain Multiagent Systems With Prescribed-Time Convergence 具有规定时间收敛的不确定多智能体系统的全分布自适应模糊弹性纳什均衡寻求

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-14 DOI: 10.1109/TFUZZ.2025.3547754

Yuan-Xin Li;Bo Xu;Shaocheng Tong

{"title":"Fully Distributed Adaptive Fuzzy Resilient Nash Equilibrium Seeking for Uncertain Multiagent Systems With Prescribed-Time Convergence","authors":"Yuan-Xin Li;Bo Xu;Shaocheng Tong","doi":"10.1109/TFUZZ.2025.3547754","DOIUrl":"10.1109/TFUZZ.2025.3547754","url":null,"abstract":"This article focuses on the prescribed-time fully distributed Nash equilibrium seeking (PT-FDNES) against denial-of-service attacks of multiagent systems (MASs), where the action of each agent is described by high-order uncertain nonlinear dynamics. Specifically, the fuzzy logic system technique is utilized to approximate the unknown nonlinear functions existing in the agents' dynamics, where a single parameter updating law is proposed by estimating the norm of fuzzy optimal weight vectors to further reduce the computational cost. To deal with the difficulty where the attack-induced topologies may destabilize the NE seeking performance, a novel PT function together with a resetting mechanism is delicately designed. Then, a PT-FDNES with adaptive gains is developed by incorporating the resetting mechanism to search for the NE independent of any global information. Subsequently, a novel PT fuzzy adaptive tracking control law is designed in the celebrated backstepping structure. Based on the Lyapunov stability theory, we prove that actions of the MAS converge to the NE within the PT and all the closed-loop system signals remain bounded. At last, an example is given to illustrate the effectiveness of the proposed algorithm.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2109-2121"},"PeriodicalIF":10.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635722","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

Hierarchical Fuzzy Topological System for High-Dimensional Data Regression Problems 高维数据回归问题的层次模糊拓扑系统

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-13 DOI: 10.1109/TFUZZ.2025.3549791

Mengxue Yao;Taoyan Zhao;Jiangtao Cao;Jinna Li

{"title":"Hierarchical Fuzzy Topological System for High-Dimensional Data Regression Problems","authors":"Mengxue Yao;Taoyan Zhao;Jiangtao Cao;Jinna Li","doi":"10.1109/TFUZZ.2025.3549791","DOIUrl":"10.1109/TFUZZ.2025.3549791","url":null,"abstract":"High-dimensional data regression presents significant challenges due to factors such as strong nonlinearity among features, an excessive number of intermediate variables, and rule explosion, all of which hinder the model's ability to capture complex features and achieve low regression accuracy. This article proposes a method for high-dimensional data regression using a hierarchical fuzzy topological system (HFTS). The HFTS adopts a modular design, where each layer consists of an independent fuzzy logic system, enabling flexible operation based on feature distribution and output requirements. It utilizes a graph neural network based hierarchical feature classification approach to group high-dimensional data, mapping features into nodes and establishing edges based on similarity. This process creates a topological structure that facilitates high-density feature representation through neighborhood aggregation. HFTS introduces a cross-layer rule-sharing mechanism and an interpolation expansion algorithm to smooth fuzzy rules, thereby reducing interaction complexity. Additionally, an adaptive weight adjustment strategy dynamically optimizes feature importance, enhancing both robustness and predictive accuracy. When applied to eleven KEEL regression datasets, HFTS demonstrates superior accuracy, effectively addressing high-dimensional interactions while maintaining a balance between interpretability and performance.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2084-2095"},"PeriodicalIF":10.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618236","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

Sliding Mode Learning Control for Interconnected Nonlinear Systems via Data-Driven Self-Triggered Fuzzy Approach 基于数据驱动自触发模糊方法的互联非线性系统滑模学习控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-13 DOI: 10.1109/TFUZZ.2025.3551374

Fanbiao Li;Tengda Wang;Yiyun Zhao;Tingwen Huang;Chunhua Yang;Weihua Gui

引用次数: 0

Double-Layer Fuzzy Neural Network Based Optimal Control for Wastewater Treatment Process 基于双层模糊神经网络的污水处理过程最优控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-12 DOI: 10.1109/TFUZZ.2025.3550810

Junfei Qiao;Dingyuan Chen;Cuili Yang;Dapeng Li

{"title":"Double-Layer Fuzzy Neural Network Based Optimal Control for Wastewater Treatment Process","authors":"Junfei Qiao;Dingyuan Chen;Cuili Yang;Dapeng Li","doi":"10.1109/TFUZZ.2025.3550810","DOIUrl":"10.1109/TFUZZ.2025.3550810","url":null,"abstract":"To obtain the effective purification performance in wastewater treatment process (WWTP), the optimal control is an important method to guarantee the effluent quality reaching the standard and improve the treatment efficiency. The concentrations of dissolved oxygen (DO) and nitrate nitrogen (NO<inline-formula><tex-math>$_{text{3}}$</tex-math></inline-formula>-N) are primary metrics that impact effluent quality, which is needed to be stably tracking controlled for achieving optimal performance in WWTP. Therefore, the double-layer fuzzy neural network (FNN)-based optimal control method with multivariable is proposed. First, considering the dynamic characteristic of WWTP, the FNN-based actor network is exploited to approximate the unknown dynamic information. Subsequently, the FNN-based critic network is integrated to minimize the cost function of DO and NO<inline-formula><tex-math>$_{text{3}}$</tex-math></inline-formula>-N concentrations, which is composed of the control error and the control variable. Then, to guarantee the stability of the optimal controller, the Lyapunov function is constructed through backstepping method to analyze the control system performance. Finally, the optimality and effectiveness of the control system with multivariable are verified via the simulation experiments in benchmark simulation model 1.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2062-2073"},"PeriodicalIF":10.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608070","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

Distributed Estimator-Based Fuzzy Containment Control for Nonlinear Multiagent Systems With Deferred Constraints 时滞约束非线性多智能体系统的分布估计模糊包容控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-12 DOI: 10.1109/TFUZZ.2025.3550864

Hui Ma;Qi Zhou;Hongru Ren;Zhenyou Wang

引用次数: 0

ODMGIS: An Outlier Detection Method Based on Multigranularity Information Sets ODMGIS：基于多粒度信息集的离群点检测方法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-12 DOI: 10.1109/TFUZZ.2025.3550749

Pengfei Zhang;Zhaoxuan He;Dexian Wang;Tao Jiang;Baolin Li;Jia Liu;Wei Huang;Tianrui Li

{"title":"ODMGIS: An Outlier Detection Method Based on Multigranularity Information Sets","authors":"Pengfei Zhang;Zhaoxuan He;Dexian Wang;Tao Jiang;Baolin Li;Jia Liu;Wei Huang;Tianrui Li","doi":"10.1109/TFUZZ.2025.3550749","DOIUrl":"10.1109/TFUZZ.2025.3550749","url":null,"abstract":"In the realm of data mining, outlier detection has emerged as a pivotal research focus, aimed at uncovering anomalies within datasets to extract meaningful and valuable insights. The objective is to leverage data mining methodologies to pinpoint anomalies within datasets, thereby revealing crucial and enlightening information. Herein, we introduce a groundbreaking outlier detection methodology, ODMGIS, that seamlessly integrates multigranularity representation and information set concepts to devise the multigranularity information set (MGIS) model. This model adeptly characterizes the distribution patterns of data points. First, we employ entropy function and their complementary function as measurement tools to accurately quantify the inherent uncertainty in data with different distributions, and considers the sum of the two as a comprehensive representation of the overall uncertainty of the information source. Subsequently, an outlier score model is constructed based on MGIS, which can deeply characterize the degree of outlierness of samples, thereby effectively identifying abnormal points in the dataset. During validation, ODMGIS was rigorously tested on practical datasets from medicine and bioinformatics, and its performance was benchmarked against both traditional and the state-of-the-art algorithms, showcasing substantial benefits. This research not only contributes a fresh perspective to outlier detection, but also sparks innovative avenues for exploring and advancing the granular computing theory.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2050-2061"},"PeriodicalIF":10.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608068","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

Nash Equilibrium Solutions for Switched Nonlinear Systems: A Fuzzy-Based Dynamic Game Method 切换非线性系统的纳什均衡解：一种基于模糊的动态博弈方法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-10 DOI: 10.1109/TFUZZ.2025.3549879

Yan Zhang;Zhengrong Xiang

引用次数: 0