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

Predictor-Based Fuzzy Optimal Tracking Control With Enhanced Transient Estimation and Learning Performance for Nonlinear Systems 基于预测器的非线性系统模糊最优跟踪控制及其增强的暂态估计和学习性能

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-11 DOI: 10.1109/TFUZZ.2024.3514876

Shuhang Yu;Huaguang Zhang;Jiayue Sun;Xiaohui Yue

{"title":"Predictor-Based Fuzzy Optimal Tracking Control With Enhanced Transient Estimation and Learning Performance for Nonlinear Systems","authors":"Shuhang Yu;Huaguang Zhang;Jiayue Sun;Xiaohui Yue","doi":"10.1109/TFUZZ.2024.3514876","DOIUrl":"10.1109/TFUZZ.2024.3514876","url":null,"abstract":"In this article, a finite-time learning-based optimal tracking problem for nonlinear systems with preassigned performance constraint is investigated. By designing a state predictor, a fuzzy approximator driven by prediction errors rather than tracking errors is formulated to precisely compensate the effect of the unknown uncertainties. The design realizes a decoupling of control and estimation loops, effectively ensuring transient approximation performance and avoiding chattering induced by nonzero initial tracking errors. Then, based on the estimated components, a robust steady-state control scheme embedded with a prescribed performance mechanism is tailored to guarantee that the output state can converge to a predefined range within a preassigned time. This endows the designed controller with a specified time tracking capability independence on control parameters. To make a tradeoff between tracking precision and energy cost, a finite-time learning-based optimal control policy is exploited by utilizing adaptive dynamic programming technique to serve as an adaptive supplementary controller, where single critic neural network is trained for acquiring the solution of the Hamilton–Jacobi–Bellman equation. Compared with the traditional gradient descent method, the established learning law is updated by introducing an auxiliary variable, which enhances learning performance and guarantees finite-time convergence of adaptive weights. Simulation examples examine the effectiveness and superiority of the suggested scheme.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1231-1240"},"PeriodicalIF":10.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809199","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 Degradation Perception Based on CLIP Prior for All-in-One Image Restoration 基于CLIP先验的自适应模糊退化感知一体化图像恢复

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-11 DOI: 10.1109/TFUZZ.2024.3512864

Mingwen Shao;Yuexian Liu;Yuanshuo Cheng;Yecong Wan;Changzhong Wang

{"title":"Adaptive Fuzzy Degradation Perception Based on CLIP Prior for All-in-One Image Restoration","authors":"Mingwen Shao;Yuexian Liu;Yuanshuo Cheng;Yecong Wan;Changzhong Wang","doi":"10.1109/TFUZZ.2024.3512864","DOIUrl":"10.1109/TFUZZ.2024.3512864","url":null,"abstract":"Despite substantial progress, the existing all-in-one image restoration methods still lack the ability to adaptively sense and accurately represent degradation information, thus hindering the enhancement of restoration performance. In addition, due to the large uncertainty and fuzziness of the data distribution in real scenarios compared to the training data, the model's generalization ability is often limited. To address the above issues, we propose a novel adaptive fuzzy degradation perception approach based on fuzzy theory that includes two tactics: 1) Fuzzy Degradation Perceiver (FDP); and 2) Test-time Self-supervised Prompt Fine-tuning (TSPF). On the one hand, we introduce the FDP, which leverages the rich visual language prior knowledge in CLIP to learn the prompt representations of different degradations. These prompts are regarded as semantic representations of various degradation fuzzy sets, achieving adaptive degradation perception by computing the degrees of membership between input images and the fuzzy sets. On the other hand, we propose the TSPF strategy, which is capable of self-supervised optimization of degraded fuzzy sets according to real-world scenarios during testing. This strategy improves the model's ability to perceive and represent the degraded information in data with real-world distributions. Thanks to the above key strategies, our method significantly improves degradation perception capability and image restoration quality while exhibiting excellent generalization in complex real-world scenarios. Extensive experiments on multiple benchmark datasets confirm that our approach achieves state-of-the-art performance in all-in-one image restoration.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1219-1230"},"PeriodicalIF":10.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142809200","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

2024 Index IEEE Transactions on Fuzzy Systems Vol. 32 2024索引IEEE模糊系统学报Vol. 32

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-11 DOI: 10.1109/TFUZZ.2024.3514372

引用次数: 0

Lexicographic Minimum Solution to the System of Fuzzy Relation Inequalities With Addition-Min-Product Composition 具有加法-最小积组合的模糊关系不等式系统的字典最小解

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-11 DOI: 10.1109/TFUZZ.2024.3514746

Qianyu Shu;Guocheng Zhu;Xingming Wang;Xiaopeng Yang

引用次数: 0

A Novel Dynamic Event-Triggered Fuzzy Adaptive Prescribed-Time Tracking Control for Nonstrict Feedback Nonlinear Systems With Unknown Control Directions 控制方向未知的非严格反馈非线性系统的动态事件触发模糊自适应规定时间跟踪控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-09 DOI: 10.1109/TFUZZ.2024.3512881

Yana Yang;Xiaoshi Liu;Changchun Hua;Xiaolei Li

{"title":"A Novel Dynamic Event-Triggered Fuzzy Adaptive Prescribed-Time Tracking Control for Nonstrict Feedback Nonlinear Systems With Unknown Control Directions","authors":"Yana Yang;Xiaoshi Liu;Changchun Hua;Xiaolei Li","doi":"10.1109/TFUZZ.2024.3512881","DOIUrl":"10.1109/TFUZZ.2024.3512881","url":null,"abstract":"Control design for nonstrict feedback nonlinear systems (NSFNS) with nonlower triangular structure may encounter algebraic loop problem, which is a significant, challenging, yet complicate issue to develop a controller with unknown control directions, especially in the presence of external disturbances and time-varying parameters. To solve these issues, unlike existing studies on prescribed-time stability under unknown control directions, a novel zero-error prescribed-time tracking control scheme is proposed for NSFNS based on the fuzzy logic approximation and adaptive technique. Moreover, a new switching event-triggered mechanism that focuses on triggering strategies and conditions to minimize resource wastage from continuous controller sampling is constructed. This approach effectively reduces the frequency of sampling and energy loss within the controller. In addition, the boundedness of the Lyapunov function is analyzed using invariant set theory. It demonstrates that the tracking error converges to zero within a user-defined time, which simultaneously ensures all signals of the closed-loop system be bounded and Zeno-free phenomenon. Finally, the efficacy of the proposed control algorithm is validated through numerical simulation results.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1192-1204"},"PeriodicalIF":10.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797164","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 Consistent Pseudoinverse Control for a Class of Constrained Nonlinear Multiagent Systems and Its Application 一类约束非线性多智能体系统的自适应模糊一致伪逆控制及其应用

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-09 DOI: 10.1109/TFUZZ.2024.3514104

Guoqiang Zhu;Xuecheng Zhang;Xiuyu Zhang;Chenguang Yang;Xinkai Chen;Chun-Yi Su

{"title":"Adaptive Fuzzy Consistent Pseudoinverse Control for a Class of Constrained Nonlinear Multiagent Systems and Its Application","authors":"Guoqiang Zhu;Xuecheng Zhang;Xiuyu Zhang;Chenguang Yang;Xinkai Chen;Chun-Yi Su","doi":"10.1109/TFUZZ.2024.3514104","DOIUrl":"10.1109/TFUZZ.2024.3514104","url":null,"abstract":"This article focuses on a type of hysteretic nonlinear multiagent system with state constraints. It introduces an adaptive consensus pseudoinverse control scheme based on fuzzy logic systems and barrier Lyapunov functions (BLFs), ensuring that all states of the closed-loop system are strictly confined within predefined constraints. The main features of this study are as follows. First, in order to cope with the hysteresis nonlinearity present in the actuators of multiagent systems, a pseudoinverse control algorithm has been proposed. This approach avoids the direct construction of a hysteresis inverse model. It achieves control by searching for actual control signals in temporary control laws, and it does not rely on the precise model of the system. Therefore, it can adapt more flexibly to the uncertainties in the system. Second, within the framework of this control strategy, the seamless integration of fuzzy control, BLFs, and the hysteresis pseudoinverse algorithm effectively addresses the full-state constraint control issue in the Preisach hysteresis model. The control signals are elegantly represented in the form of a double-integral function, offering a potent solution to the constraints inherent in the Preisach hysteresis model. Third, a multiagent collaborative control experimental platform was established, incorporating quadcopter autonomous aerial vehicles and ground mobile robots. Experimental validation of the proposed control scheme against traditional control approaches demonstrated the effectiveness of the proposed control strategy in practical applications.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1205-1218"},"PeriodicalIF":10.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797163","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

Multiscale Group Consensus With Heterogeneous Preference Structures Based on Fuzzy Social Networks and Prospect-Regret Theory 基于模糊社会网络和期望-后悔理论的异质性偏好结构的多尺度群体共识

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-09 DOI: 10.1109/TFUZZ.2024.3512773

Yibin Xiao;Jianming Zhan;Zeshui Xu;Rosa M. Rodríguez

引用次数: 0

Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics 工业数据分析中的模糊递归随机配置网络

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-05 DOI: 10.1109/TFUZZ.2024.3511695

Dianhui Wang;Gang Dang

{"title":"Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics","authors":"Dianhui Wang;Gang Dang","doi":"10.1109/TFUZZ.2024.3511695","DOIUrl":"10.1109/TFUZZ.2024.3511695","url":null,"abstract":"This article presents a novel neuro-fuzzy model, termed fuzzy recurrent stochastic configuration networks (F-RSCNs), for industrial data analytics. Unlike the original recurrent stochastic configuration network (RSCN), the proposed F-RSCN is constructed by multiple subreservoirs, and each subreservoir is associated with a Takagi–Sugeno–Kang (TSK) fuzzy rule. Through this hybrid framework, first, the interpretability of the model is enhanced by incorporating fuzzy reasoning to embed the prior knowledge into the network. Then, the parameters of the neuro-fuzzy model are determined by the recurrent stochastic configuration (RSC) algorithm. This scheme not only ensures the universal approximation property and fast learning speed of the built model but also overcomes uncertain problems, such as unknown dynamic orders, arbitrary structure determination, and the sensitivity of learning parameters in modeling nonlinear dynamics. Finally, an online update of the output weights is performed using the projection algorithm, and the convergence analysis of the learning parameters is given. By integrating TSK fuzzy inference systems into RSCNs, F-RSCNs have strong fuzzy inference capability and can achieve sound performance for both learning and generalization. Comprehensive experiments show that the proposed F-RSCNs outperform other classical neuro-fuzzy and nonfuzzy models, demonstrating great potential for modeling complex industrial systems.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1178-1191"},"PeriodicalIF":10.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782804","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

Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction–Diffusion Neural Networks 基于协议的T-S模糊反应-扩散神经网络采样数据控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-04 DOI: 10.1109/TFUZZ.2024.3511120

Jun Cheng;Na Liu;Leszek Rutkowski;Jinde Cao;Huaicheng Yan

{"title":"Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction–Diffusion Neural Networks","authors":"Jun Cheng;Na Liu;Leszek Rutkowski;Jinde Cao;Huaicheng Yan","doi":"10.1109/TFUZZ.2024.3511120","DOIUrl":"10.1109/TFUZZ.2024.3511120","url":null,"abstract":"This study explores the protocol-based sampled-data control for T-S fuzzy reaction–diffusion neural networks (RDNNs) with nonhomogeneous sojourn probabilities (NSPs). By incorporating a deterministic switching signal, a new framework of NSPs is developed to characterize the random behaviors of fuzzy RDNNs. Unlike previous studies, this work introduces multiasynchronous switching among fuzzy RDNNs, triggering conditions, and controllers using dynamic asynchrony models, effectively capturing mode transitions through NSPs and detection probabilities. An improved adaptive event-triggered protocol is created by integrating fuzzy rules and detection probability information. Moving beyond traditional time-domain sampled-data control strategies, a space–time sampled-data control approach is proposed to significantly reduce communication load. Benefiting from Lyapunov theory, criteria are attained for ensuring the mean-square exponential stability of the systems under consideration. Ultimately, the proposed space–time sampled-data control strategy is validated through a simulation example, highlighting its effectiveness and superiority.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1168-1177"},"PeriodicalIF":10.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776600","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 Computational Intelligence Society Information IEEE计算智能学会信息

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-02 DOI: 10.1109/TFUZZ.2024.3492757

引用次数: 0