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

An Equilibrium Approach to Clustering: Surpassing Fuzzy C-Means on Imbalanced Data 一种均衡聚类方法：在不平衡数据上超越模糊c均值

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-28 DOI: 10.1109/tfuzz.2025.3590508

Yudong He

引用次数: 0

Predictor-Based Event-Triggered Optimized Control for Uncertain Nonlinear Systems With Time Delays: A Reinforcement Learning Approach 不确定非线性时滞系统基于预测器的事件触发优化控制：一种强化学习方法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-25 DOI: 10.1109/TFUZZ.2025.3592833

Ping Li;Li Fu;Zhibao Song;Zhen Wang

引用次数: 0

Affective Evaluation Based on Gamified Experience and Cascaded Fuzzy Reasoning for Wrist Rehabilitation 基于游戏化经验和级联模糊推理的腕部康复情感评价

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-23 DOI: 10.1109/TFUZZ.2025.3592178

Weihua Lu;Yingying Fang;Wenxin He;Yicha Zhang;Xiaochuan Wang

{"title":"Affective Evaluation Based on Gamified Experience and Cascaded Fuzzy Reasoning for Wrist Rehabilitation","authors":"Weihua Lu;Yingying Fang;Wenxin He;Yicha Zhang;Xiaochuan Wang","doi":"10.1109/TFUZZ.2025.3592178","DOIUrl":"10.1109/TFUZZ.2025.3592178","url":null,"abstract":"In order to establish the link between rehabilitation experience and patients’ emotional feedback to assist decision making in a wrist rehabilitation program, this article integrates a gamified experience and proposes an affective evaluation method based on cascaded fuzzy reasoning. Primarily, gamified interactive tasks are developed for wrist rehabilitation training, and the prototype of a rehabilitation interactive system is built to collect multidimensional physiological signals (MPSs). Subsequently, with the MPS as input and arousal valence (AV) as output, fuzzy reasoning rule I is defined to establish the MPS-AV model and calculate the value of AV. Besides, with AV as input and emotion label (EL) as output, fuzzy reasoning rule II is defined to establish the AV-EL model and visually analyze the emotional state of patients. Finally, the task completion rate of rehabilitation interaction behavior is calculated based on behavior coding, and the effectiveness of the proposed affective evaluation method is verified by integrating subjective evaluation. The gamified rehabilitation experience and the affective evaluation method based on cascaded fuzzy reasoning provide methods and experiences for wrist-rehabilitation-related products and services, and this protocol can also be generalized in the field of rehabilitation digital therapy.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"2950-2961"},"PeriodicalIF":11.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694071","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 Weighted Regularization for Fractional-Order Nonlinear Multiagent Systems Under Stackelberg–Nash Game Stackelberg-Nash对策下分数阶非线性多智能体系统的模糊加权正则化

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-23 DOI: 10.1109/TFUZZ.2025.3592271

Qian Kang;Dengxiu Yu;C. L. Philip Chen

{"title":"Fuzzy Weighted Regularization for Fractional-Order Nonlinear Multiagent Systems Under Stackelberg–Nash Game","authors":"Qian Kang;Dengxiu Yu;C. L. Philip Chen","doi":"10.1109/TFUZZ.2025.3592271","DOIUrl":"10.1109/TFUZZ.2025.3592271","url":null,"abstract":"This study focuses on achieving hierarchical optimal synchronization in a fractional-order nonlinear multiagent system (FONMAS) composed of a single leader and multiple followers, analyzed within the framework of the Stackelberg–Nash game theory. The leader makes decisions by anticipating the optimal responses of all followers, whereas each follower concurrently reacts optimally to the leader’s strategy by engaging in a Nash game. To obtain the optimal control policy of the FONMAS, a regularized fuzzy reinforcement learning approach is proposed. First, a fractional-order (FO) Hamilton–Jacobi–Bellman (HJB) equation in coupled form is formulated, which serves as the basis for deriving the optimal control policies of both the leader and the followers. It is further proven that these strategies constitute a Stackelberg–Nash equilibrium. Next, due to the asymmetry among agents, solving the FO coupled HJB equations becomes challenging. To address this, a hierarchical learning framework grounded in FO value iteration is proposed, which depends solely on partial knowledge of the system dynamics. We demonstrate that, given mild coupling assumptions, this method converges asymptotically to equilibrium policies. Furthermore, a regularized actor–critic framework with fuzzy logic is employed to estimate the cost function and optimal control policy, and the FO weight update rules are developed by formulating a Lyapunov function for the optimal fuzzy weight deviation, ensuring convergence to optimal weight values. Ultimately, both theoretical analysis and simulation results support the efficiency of the proposed method.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3345-3359"},"PeriodicalIF":11.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694192","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

MAFRO: Optimal-Granularity Fuzzy Decision Rule-Based Classification Architecture for Attribute Unlearning 基于最优粒度模糊决策规则的属性去除分类体系结构

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-22 DOI: 10.1109/TFUZZ.2025.3586297

Jiande Huang;Yuhui Deng;Yi Zhou;Qifen Yang;Geyong Min

{"title":"MAFRO: Optimal-Granularity Fuzzy Decision Rule-Based Classification Architecture for Attribute Unlearning","authors":"Jiande Huang;Yuhui Deng;Yi Zhou;Qifen Yang;Geyong Min","doi":"10.1109/TFUZZ.2025.3586297","DOIUrl":"10.1109/TFUZZ.2025.3586297","url":null,"abstract":"Recently, many laws and regulations have granted users the right to be forgotten, i.e., the right to require data controllers to delete user data. Various methods for machine unlearning have been proposed to remove individual data points. However, they do not scale to the scenarios where larger groups of features are to be removed. To address this challenge, we propose MAFRO, an optimal-granularity fuzzy decision rule–based classifier that accelerates unlearning via influence functions. Building on granular computing (GrC), MAFRO first selects a minimal reduct of attributes, then constructs fuzzy granules with a Gaussian membership function to extract concise decision rules and realizes unlearning through the influence function. Specifically, instead of training with the full set of attributes, we use the reduct, a minimal subset of attributes that can classify the data with the same accuracy as the full set of attributes. Next, we extract fuzzy rules based on the reduct. Finally, fusing the generated rules establishes the linear model with strongly convex loss functions. In this way, MAFRO can quantify the divergence caused by attribute deleting and update the model without retraining it, thereby adapting the influence of data removal on the model and accelerating the unlearning process. We conduct extensive experiments to evaluate MAFRO on 10 typical datasets in terms of performance and unlearning speed. We compare MAFRO with the state-of-the-art algorithms. Experimental results demonstrate that MAFRO enhances accuracy by an average of 6.96%, and achieves up to 236× speedup for attribute unlearning tasks.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3240-3252"},"PeriodicalIF":11.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684605","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 Distributed Optimal Event-Triggered Control for High-Order Nonlinear Multiagent Systems 高阶非线性多智能体系统的自适应模糊分布式最优事件触发控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-18 DOI: 10.1109/TFUZZ.2025.3590345

Mengyuan Cui;Wenjun Zhang;Shaocheng Tong

引用次数: 0

Optimal Containment Control of Heterogeneous Multiagent Systems With Unknown Dynamics and Actuator Faults via Fuzzy Reinforcement Learning 基于模糊强化学习的未知动力学和执行器故障异构多智能体系统最优包容控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-18 DOI: 10.1109/TFUZZ.2025.3590449

Donghao Liu;Zehui Mao;Bin Jiang;Peng Shi;Yajie Ma

{"title":"Optimal Containment Control of Heterogeneous Multiagent Systems With Unknown Dynamics and Actuator Faults via Fuzzy Reinforcement Learning","authors":"Donghao Liu;Zehui Mao;Bin Jiang;Peng Shi;Yajie Ma","doi":"10.1109/TFUZZ.2025.3590449","DOIUrl":"10.1109/TFUZZ.2025.3590449","url":null,"abstract":"This article investigates the fuzzy optimal fault-tolerant containment control (FTCC) problem for heterogeneous nonlinear multiagent systems with unknown dynamics and actuator faults in which the unknown dynamics exhibit nonlinear behavior. To address this problem, a performance index comprising local containment error, control energy, and fault effects, is first formulated under the zero-sum differential game, where controllers and faults are treated as opposing players with different signs. Subsequently, improved generalized fuzzy hyperbolic model-based approximation techniques are used to identify unknown dynamics and learn optimal FTCC policies incorporating reinforcement learning (RL). Specifically, to enhance the weight convergence performance and relax the traditional persistence of excitation condition, a practical fixed-time identifier is developed based on the generalized fuzzy hyperbolic model by integrating practical fixed-time system identification technique with experience replay technique. Then, using the generalized fuzzy hyperbolic model as the critic, a fuzzy-RL algorithm with experience replay is developed to learn optimal FTCC policies from the coupled Hamilton–Jacobi–Isaacs equations. The containment error and the critic approximation error are ensured to be ultimately uniformly bounded using the Lyapunov method. Finally, the validity of the control scheme is verified via a numerical simulation.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3331-3344"},"PeriodicalIF":11.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678140","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

Improved Interval Type-II Fuzzy Broad MPC Method for Furnace Temperature of Municipal Solid Waste Incineration Process 城市生活垃圾焚烧过程炉温的改进区间ⅱ型模糊广义MPC法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-18 DOI: 10.1109/tfuzz.2025.3590654

Bokang Wang, Jian Tang, Hao Tian, Wen Yu, Junfei Qiao

引用次数: 0

USTformer: Transformer Based on Multi-Granularity Attention for Knowledge Completion over Uncertain Spatiotemporal Knowledge Graph USTformer：基于多粒度关注的不确定时空知识图知识补全变压器

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-18 DOI: 10.1109/tfuzz.2025.3590579

Xiaowen Zhang, Li Yan, Zongmin Ma

引用次数: 0

A Unified Practical Predefined-Time Interval Type-2 Fuzzy NN-Based Fault-Tolerant Control for Robotic Manipulators 基于统一实用的预定义时间区间2型模糊神经网络的机器人机械臂容错控制

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-07-15 DOI: 10.1109/TFUZZ.2025.3588146

Tao Zhao;Shiyu Tian;Hong Cheng

{"title":"A Unified Practical Predefined-Time Interval Type-2 Fuzzy NN-Based Fault-Tolerant Control for Robotic Manipulators","authors":"Tao Zhao;Shiyu Tian;Hong Cheng","doi":"10.1109/TFUZZ.2025.3588146","DOIUrl":"10.1109/TFUZZ.2025.3588146","url":null,"abstract":"Fast response and safety operation are essential requirements for the tracking control of robotic manipulators. In this article, a unified predefined-time self-organizing interval type-2 fuzzy neural network control (SOIT2FNNC) framework is presented for robotic manipulators subject to actuator failures and uncertainties. Such a framework operates in a parallel structure where the model-free predefined-time controller guarantees the transient performance while the proposed network controller provides appropriate torques to handle failures and uncertainties, which leads to a solution for both normal and faulty conditions. Significant features of this study are that the control design does not depend on any information about system dynamics, and theoretically, the predefined-time convergence is accomplished by means of the online parameter learning algorithm. Moreover, a hierarchical self-organizing algorithm is embedded in the proposed network controller to overcome the network structure complexity and the input partition problem. Both numerical simulation and experiment results utilizing artificial faults are implemented to demonstrate the superiority of the proposed control scheme.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 9","pages":"3267-3280"},"PeriodicalIF":11.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639658","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