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

Adaptive Incremental Broad Learning System Based on Interval Type-2 Fuzzy Set with Automatic Determination of Hyperparameters 基于区间2型模糊集超参数自动确定的自适应增量广义学习系统

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-16 DOI: 10.1109/tfuzz.2025.3530458

Haijie Wu, Weiwei Lin, Yuehong Chen, Fang Shi, Wangbo Shen, C. L. Philip Chen

引用次数: 0

Brain-Inspired Fuzzy Graph Convolution Network for Alzheimer's Disease Diagnosis Based on Imaging Genetics Data 基于影像遗传学数据的脑启发模糊图卷积网络阿尔茨海默病诊断

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/tfuzz.2025.3529304

Xia-An Bi, Yangjun Huang, Wenzhuo Shen, Zicheng Yang, Yuhua Mao, Luyun Xu, Zhonghua Liu

引用次数: 0

Exponential Stability of Fractional-Order Fuzzy Multilayer Networks With Short Memory and Noninstantaneous Impulses via Intermittent Control 通过间歇控制实现短记忆和非瞬时脉冲的分数阶模糊多层网络的指数稳定性

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/TFUZZ.2025.3528975

Yao Xu;Zui Chen;Wenxue Li;Yongbao Wu

引用次数: 0

Granular Sequential Three-Way Decision for Specific Decision Classes 特定决策类别的粒度顺序三向决策

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/TFUZZ.2025.3529459

Yunlong Cheng;Xiuhua Yang;Qinghua Zhang;Yabin Shao;Guoyin Wang

{"title":"Granular Sequential Three-Way Decision for Specific Decision Classes","authors":"Yunlong Cheng;Xiuhua Yang;Qinghua Zhang;Yabin Shao;Guoyin Wang","doi":"10.1109/TFUZZ.2025.3529459","DOIUrl":"10.1109/TFUZZ.2025.3529459","url":null,"abstract":"Sequential three-way decision (S3WD) is an efficient granular computing paradigm for dealing with uncertain problems. However, it is primarily oriented to all decision classes, which contradicts the fact that decisions are typically for the specific decision classes. Meanwhile, most S3WD models hide the topological structure of the granules, leading to difficulties in semantic interpretation. To address the issues, integrating model construction, attribute reduction and knowledge extraction, a general framework of granular sequential three-way decision for the specific decision classes is proposed to improve semantic interpretation and computational efficiency. First, a two-stage trisecting strategy and a GrS3WD model are proposed to integrate model construction with attribute reduction. Its main advantage is that it retains the topological structure of granules, which not only enhances semantic interpretation, but also avoids unnecessary double counting. Second, three acceleration strategies and a novel granular sequential three-way reduction (GrS3WR) algorithm are proposed to fast obtain a classification-based reduct or a class-specific reduct. Finally, the decision rules with multigranularity can be directly extracted from the concept tree generated by GrS3WR. Experimental results demonstrate that a class-specific reduct usually has fewer attributes and better classification performance than a classification-based reduct. Moreover, GrS3WR can significantly improve the computational efficiency of attribute reduction.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1650-1663"},"PeriodicalIF":10.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986686","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 Critic-Based Optimal Control of Input-Constrained Stochastic Systems via Generalized Fuzzy Hyperbolic Models 基于广义模糊双曲模型的输入约束随机系统自适应临界最优控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/TFUZZ.2024.3523898

Lulu Zhang;Huaguang Zhang;Jiayue Sun;Zhongyang Ming

引用次数: 0

On Dissipativity-Preserving for Switched Positive Takagi–Sugeno Fuzzy Delayed Systems via Switching 交换正Takagi-Sugeno模糊时滞系统的耗散保持问题

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-14 DOI: 10.1109/TFUZZ.2025.3528936

Peng Wang;Hong Sang;Chuangxia Huang;Jinde Cao;Mahmoud Abdel-Aty

{"title":"On Dissipativity-Preserving for Switched Positive Takagi–Sugeno Fuzzy Delayed Systems via Switching","authors":"Peng Wang;Hong Sang;Chuangxia Huang;Jinde Cao;Mahmoud Abdel-Aty","doi":"10.1109/TFUZZ.2025.3528936","DOIUrl":"10.1109/TFUZZ.2025.3528936","url":null,"abstract":"In this article, we tackle the problem of analyzing dissipativity, via devising switching mechanisms, for switched positive Takagi–Sugeno (T–S) fuzzy systems with time-varying delay. To leverage the positivity properties of state, input, and output variables, a novel concept of dissipativity is developed, focusing on linear supply rates and linear copositive storage functionals. When state information is available, the state dependent switching mechanism satisfying a dwell time constraint is introduced dependent on the constructed time-varying multiple linear copositive storage functionals. This mechanism allows for solving the dissipativity issue for the entire system without imposing any solvability requirements on subsystems and reduces the switching frequency. In cases, where state information is unavailable, dissipativity is ensured by a dwell-time dependent switching mechanism. Further, all conditions guaranteeing the solvability of the problem are presented in the form of linear vector inequalities. Two simulation examples are finally offered, demonstrating that the proposed techniques are effective and superior.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1605-1616"},"PeriodicalIF":10.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981299","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 Novel Manifold Optimization Algorithm With the Dual Function and a Fuzzy Valuation Step 一种新的具有对偶函数和模糊评价步骤的流形优化算法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-14 DOI: 10.1109/TFUZZ.2024.3520238

Weiping Liu;Youfa Liu;He Li;Jingui Zou

{"title":"A Novel Manifold Optimization Algorithm With the Dual Function and a Fuzzy Valuation Step","authors":"Weiping Liu;Youfa Liu;He Li;Jingui Zou","doi":"10.1109/TFUZZ.2024.3520238","DOIUrl":"10.1109/TFUZZ.2024.3520238","url":null,"abstract":"Fuzzy mathematical theory is widely used, fuzzy optimization is a branch of fuzzy mathematical theory, the significant application area is artificial intelligence in computer science, especially machine learning (deep learning) and pattern recognition. Fuzzy mathematics, especially fuzzy optimization, has become a bridge between the manifold optimization theory and deep learning applications, which is an essential theoretical foundation. The manifold optimization algorithm employs the projection method, which is unstable. In order to resolve the problem, in this article, the theory and methodology of manifold optimization concerning real and complex spaces is fully considered. Our primary focus is on the Riemannian manifold, where a groundbreaking optimization algorithm with the dual function and a fuzzy valuation step is proposed. To accelerate the convergence and enhance the stability of the optimization algorithm, a novel learning rate is present, which is referred as bivariate gradual learning rate warm-up. A comprehensive analysis of its convergence rates is conducted in various scenarios and the experiments results substantiate our discoveries, and demonstrate the correctness and effectiveness of our devised algorithm.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1617-1626"},"PeriodicalIF":10.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981298","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

Online Streaming Feature Selection Using Bidirectional Complementarity Based on Fuzzy Gini Entropy 基于模糊基尼熵的双向互补在线流特征选择

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-13 DOI: 10.1109/TFUZZ.2025.3529466

Chucai Zhang;Zhengxiang Lu;Yongkang Zhang;Jianhua Dai

{"title":"Online Streaming Feature Selection Using Bidirectional Complementarity Based on Fuzzy Gini Entropy","authors":"Chucai Zhang;Zhengxiang Lu;Yongkang Zhang;Jianhua Dai","doi":"10.1109/TFUZZ.2025.3529466","DOIUrl":"10.1109/TFUZZ.2025.3529466","url":null,"abstract":"Online streaming feature selection has garnered widespread attention due to its efficiency and adaptability in dynamic data environments. However, existing methods primarily focus on the correlation and redundancy among features, often overlooking the complementarity between candidate and selected features. In this article, we address this gap by introducing three key innovations. First, we construct a novel metric, fuzzy Gini entropy (FGE), to measure feature uncertainty within datasets. Unlike traditional information entropy, fuzzy Gini entropy inherits the advantages of the Gini index, effectively measuring the impurity of datasets, while also being capable of handling common fuzzy environments. Accordingly, related metrics such as fuzzy joint Gini entropy, fuzzy conditional Gini entropy, and fuzzy mutual Gini information are developed. Second, we innovatively propose the concept of the bidirectional complementarity ratio, which captures the relationship between candidate features and previously selected features in online streaming feature selection. This mitigates the unfairness associated with the late arrival of features, ensuring that candidate features with a bidirectional complementary effect that outweighs their redundancy effect with the selected features are chosen. Third, we design an online streaming feature selection method named FGE-OSFS. The method evaluates streaming features through three steps: Online relevance analysis, online bidirectional complementarity analysis, and online redundancy analysis. Finally, we compare the proposed method with five state-of-the-art online streaming feature selection methods, demonstrating the effectiveness of our new approach.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1592-1604"},"PeriodicalIF":10.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974601","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

Asynchronous PID Control for T-S Fuzzy Systems Over Gilbert-Elliott Channels Utilizing Detected Channel Modes 利用检测到的信道模式实现吉尔伯特-艾略特信道上 T-S 模糊系统的异步 PID 控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-13 DOI: 10.1109/TFUZZ.2025.3528337

Yezheng Wang;Zidong Wang;Lei Zou;Quanbo Ge;Hongli Dong

{"title":"Asynchronous PID Control for T-S Fuzzy Systems Over Gilbert-Elliott Channels Utilizing Detected Channel Modes","authors":"Yezheng Wang;Zidong Wang;Lei Zou;Quanbo Ge;Hongli Dong","doi":"10.1109/TFUZZ.2025.3528337","DOIUrl":"10.1109/TFUZZ.2025.3528337","url":null,"abstract":"This article is concerned with the <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> proportional-integral-derivative (PID) control problem for Takagi-Sugeno fuzzy systems over lossy networks that are characterized by the Gilbert-Eillott model. The communication quality is reflected by the presence of two channel modes (i.e., “bad” mode and “good” mode), which switch randomly according to a Markov process. In the “bad” mode, packet dropouts are governed by a stochastic variable sequence. Considering the inaccessibility of channel modes, a mode detector is utilized to estimate the communication situation. The relationship between the actual channel mode and the estimated mode is depicted in terms of certain conditional probabilities. Moreover, a comprehensive model is constructed to represent the probability uncertainties arising from statistical errors in channel mode switching, packet dropouts, and mode detection processes. Subsequently, a robust asynchronous PID controller, based on the detected channel mode, is proposed. Sufficient conditions are then derived to ensure the mean-square stability of the closed-loop system while maintaining the desired <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> performance. Finally, the efficacy of the proposed design approach is demonstrated through a simulation example.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1555-1567"},"PeriodicalIF":10.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974706","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

Selecting Central and Divergent Samples via Leading Tree Metric Space for Semisupervised Learning 基于领先树度量空间的半监督学习中心和发散样本选择

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-13 DOI: 10.1109/TFUZZ.2025.3528400

Ji Xu;Gang Ren;Jianhang Tang;Weiping Ding;Guoyin Wang

{"title":"Selecting Central and Divergent Samples via Leading Tree Metric Space for Semisupervised Learning","authors":"Ji Xu;Gang Ren;Jianhang Tang;Weiping Ding;Guoyin Wang","doi":"10.1109/TFUZZ.2025.3528400","DOIUrl":"10.1109/TFUZZ.2025.3528400","url":null,"abstract":"The distribution of the labeled data can greatly affect the performance of a semisupervised learning (SSL) model. Most existing SSL models select the labeled data randomly and equally allocate the labeling quota among the classes, leading to considerable unstableness and degeneration of performance. This study unsupervisedly constructs a leading forest that forms another metric space, based on which it is convenient to define the fuzzy membership function to characterize central and divergent samples and select both types with fuzzy Xor logic. The labeling quota can, thus, be allocated adaptively among different classes. The proposed determinate labeling strategy can generally improve the performance for most SSLs. Especially, when combined with the kernelized large margin component analysis, it produces a novel semisupervised classification model. In addition, the multimodal issue in SSL is effectively addressed by the multigranular structure of leading forest that readily facilitates multiple local metrics learning. Extensive experimental results demonstrate that the proposed method achieved competitive efficiency and encouraging accuracy when compared with the state-of-the-art methods.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1578-1591"},"PeriodicalIF":10.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974603","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