IEEE Transactions on Fuzzy Systems最新文献_第9页

Event-Triggered Data-Driven Iterative Learning Control for Nonlinear MASs Under Switching Topologies 切换拓扑下非线性质量的事件触发数据驱动迭代学习控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-26 DOI: 10.1109/TFUZZ.2024.3523127

Shanshan Sun;Yuan-Xin Li;Zhongsheng Hou

{"title":"Event-Triggered Data-Driven Iterative Learning Control for Nonlinear MASs Under Switching Topologies","authors":"Shanshan Sun;Yuan-Xin Li;Zhongsheng Hou","doi":"10.1109/TFUZZ.2024.3523127","DOIUrl":"10.1109/TFUZZ.2024.3523127","url":null,"abstract":"This article aims to address the problem of distributed model-free adaptive iterative learning control for nonlinear discrete-time multiagent systems under switching topologies. To save valuable bandwidth in the wireless channel without sacrificing system performance, an event-triggered iterative learning control strategy is established and employed, where information is only transmitted at triggered instants. First, by virtue of the dynamic linearization technology, the controlled system can be converted into a linear model to construct the controller structure. Second, a model-free adaptive iterative learning consensus control scheme is proposed merely employing the input and output data, in which better tracking performance can be attained by learning the previous experience. Third, a dynamic event-triggered mechanism along the iteration domain is set up to deal with the limited bandwidth issue, effectively saving communication resources. Unlike most model-free adaptive control results, the constructed distributed controller is designed based on controller-dynamic-linearization approach to deal with the controller structure design issue without designing the cost function, making it more convenient in solving tracking control issues for multiagent systems under iteration-switching communication topologies, which is more suitable for the actual environment. Using graph theory and the contraction mapping principle, the convergence of tracking control errors is theoretically analyzed. Ultimately, the effectiveness of the established control schemes is illustrated through two simulation examples.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1322-1332"},"PeriodicalIF":10.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888267","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 Generalized Integrated Fuzzy-MPC With Optimal Input Excitation for Complex Systems With Industrial Applications 具有工业应用的复杂系统的最优输入激励广义集成模糊- mpc

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-25 DOI: 10.1109/tfuzz.2024.3522339

Keke Huang, Xinyu Ying, Dehao Wu, Chunhua Yang, Weihua Gui

引用次数: 0

Learning Operator-Valued Kernels From Multilabel Datesets With Fuzzy Rough Sets 基于模糊粗糙集的多标签数据集算子值核学习

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-25 DOI: 10.1109/TFUZZ.2024.3522466

Zhenxin Wang;Degang Chen;Xiaoya Che

{"title":"Learning Operator-Valued Kernels From Multilabel Datesets With Fuzzy Rough Sets","authors":"Zhenxin Wang;Degang Chen;Xiaoya Che","doi":"10.1109/TFUZZ.2024.3522466","DOIUrl":"10.1109/TFUZZ.2024.3522466","url":null,"abstract":"In multilabel learning, the precise mining and appropriate application of label correlation can improve the effectiveness and generalization of prediction models. In order to characterize label correlation more carefully, the concept of operator-valued kernel is introduced. The value of operator-valued kernel is an operator on Hilbert space, and when applied to a practical problem, the function-valued operator degenerates into a positive-definite matrix, which aims to describe the label correlation. However, existing works focus on the basic theory of operator-valued kernel, and lack way to learn specific kernel from specific datasets, thus the application of operator-valued kernel in practical problem is greatly hindered. In this article, we focus on learning operator-valued kernels with fuzzy rough sets from multilabel datasets and designing learning algorithm for multilabel classification. First, the importance distribution of feature set to different labels at each sample is measured by using kernelized fuzzy rough sets. For a single sample, label correlation matrix is constructed based on the consistency of the importance distribution of features to labels, so as to characterize the correlation information between different labels. By considering the interaction information between two label correlation matrices, the label incidence matrix between two samples is obtained. Therefore, a new operator-valued kernel is defined by using label incidence matrices as elements. This operator-valued kernel is further proved to be an entangled and transformable kernel. On the basis, the proposed operator-valued kernel is applied to develop an efficient learning algorithm for multilabel classification. The generalization error bound of the prediction function is measured by Rademacher complexity. In order to illustrate the effectiveness of our algorithm, the classification experiments and statistical analysis results on twelve multilabel datasets are provided, which are compared with seven high performance algorithms.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1311-1321"},"PeriodicalIF":10.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888269","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

Command Filter-Based Predefined-Time Adaptive Fuzzy Control for High-Order Nonlinear Interconnected Systems With Input Quantization 基于命令滤波的输入量化高阶非线性互联系统自适应模糊控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-20 DOI: 10.1109/TFUZZ.2024.3519724

Lin Zhao;Shuai Sui;Tengfei Liu;Chun-Lung Philip Chen

引用次数: 0

Deep Fuzzy System for Dual-Station Target Tracking With Azimuth and Doppler Measurements 基于方位和多普勒测量的深度模糊双站目标跟踪系统

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-18 DOI: 10.1109/TFUZZ.2024.3519767

Jianjun Huang;Xuehao Geng;Li Kang;Ge Luo

引用次数: 0

Prescribed-Time Fuzzy Optimal Containment Control for Multi-Agent Systems With Deferred Output Constraints: An Output Mask Method 具有延迟输出约束的多智能体系统的规定时间模糊最优控制：一种输出掩模方法

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-18 DOI: 10.1109/tfuzz.2024.3519720

Xiaona Song, Peng Sun, Shuai Song, Choon Ki Ahn

引用次数: 0

Dynamic Event-Triggered-Based Fuzzy Adaptive Pinning Control for Multiagent Systems With Output Saturation 输出饱和多代理系统的基于事件触发的动态模糊自适应插销控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-17 DOI: 10.1109/TFUZZ.2024.3519186

Hongru Ren;Liang Cao;Hui Ma;Hongyi Li

引用次数: 0

An Improved Fuzzy Recurrent Stochastic Configuration Network for Modeling Nonlinear Systems 一种用于非线性系统建模的改进模糊递归随机组态网络

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-17 DOI: 10.1109/TFUZZ.2024.3513394

Gang Dang;Dianhui Wang

{"title":"An Improved Fuzzy Recurrent Stochastic Configuration Network for Modeling Nonlinear Systems","authors":"Gang Dang;Dianhui Wang","doi":"10.1109/TFUZZ.2024.3513394","DOIUrl":"10.1109/TFUZZ.2024.3513394","url":null,"abstract":"Fuzzy recurrent stochastic configuration networks (F-RSCNs) have shown great potential in modeling nonlinear dynamic systems due to their high learning efficiency, less human intervention, and universal approximation capability. However, their remarkable performance is accompanied by a lack of theoretical guidance regarding parameter selection in fuzzy inference systems, making it challenging to obtain the optimal fuzzy rules. In this article, we propose an improved version of F-RSCNs termed IF-RSCNs for better model performance. Unlike traditional neuro-fuzzy models, IF-RSCNs do not rely on a fixed number of fuzzy rules. Instead, each fuzzy rule is associated with a subreservoir, which is incrementally constructed in the light of a sub-reservoir mechanism to ensure the adaptability and universal approximation property of the built model. Through this hybrid framework, the interpretability of the network is enhanced by performing fuzzy reasoning, and the parameters of both fuzzy systems and neural networks are determined using the recurrent stochastic configuration (RSC) algorithm, which inherits the fast learning speed and strong approximation ability of RSCNs. In addition, an online update of readout weights using the projection algorithm is implemented to handle complex dynamics, and the convergence analysis of the learning parameters is provided. Comprehensive experiments demonstrate that our proposed IF-RSCNs outperform other classical neuro-fuzzy and nonfuzzy models in terms of learning and generalization performance, highlighting their effectiveness in modeling nonlinear systems.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1265-1276"},"PeriodicalIF":10.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840957","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-Rough Bireducts With Supervised Multiscale Granulation 有监督的多尺度粒化模糊粗糙双环产品

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-16 DOI: 10.1109/TFUZZ.2024.3518473

Zhihong Wang;Hongmei Chen;Huming Liao;Tengyu Yin;Biao Xiang;Shi-Jinn Horng;Tianrui Li

{"title":"Fuzzy-Rough Bireducts With Supervised Multiscale Granulation","authors":"Zhihong Wang;Hongmei Chen;Huming Liao;Tengyu Yin;Biao Xiang;Shi-Jinn Horng;Tianrui Li","doi":"10.1109/TFUZZ.2024.3518473","DOIUrl":"10.1109/TFUZZ.2024.3518473","url":null,"abstract":"The inherent characteristics involved in data can be mined from multi-scale information systems by extracting information from different value levels of features. In real applications, noise data and irrelevant or redundant features affect the generality of learning models. Therefore, keeping meaningful features and avoiding the effect of noise is essential for feature selection in a multi-scale information system. In bireduct, multi-scale granulation can be used to characterize the importance and correlation of features at different scales. However, little work has taken the distribution of multi-scale data into account when granulating it. In addition, these approaches focus on solving the task of multi-scale data reduction only from the dimension perspective. To this end, a fuzzy-rough bireduct with supervised multi-scale granulation (FrBSmg) is proposed. First, the supervised multi-scale fuzzy granulation based on data distribution is constructed. Then, scaled uncertainty measures are defined to describe the fuzzy relevance of each feature. Furthermore, the global and local distributions of a sample are characterized simultaneously based on the positive region, which can reflect the degree of a sample belonging to some class, and the supervised fuzzy similarity relation can describe the degree of a sample belonging to its class. A strategy of Feature-Correlated Selection and Sample-Noisy Removal is devised for bireduct. Finally, the experimental results on twenty-one public datasets show the effectiveness of FrBSmg.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1253-1264"},"PeriodicalIF":10.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142832335","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 Flexible Prescribed Performance Control for Input Saturated Nonlinear Systems 输入饱和非线性系统的滑动柔性规定性能控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-12-12 DOI: 10.1109/TFUZZ.2024.3516132

Yangang Yao;Yu Kang;Yunbo Zhao;Jieqing Tan;Lichuan Gu;Guolong Shi

{"title":"Sliding Flexible Prescribed Performance Control for Input Saturated Nonlinear Systems","authors":"Yangang Yao;Yu Kang;Yunbo Zhao;Jieqing Tan;Lichuan Gu;Guolong Shi","doi":"10.1109/TFUZZ.2024.3516132","DOIUrl":"10.1109/TFUZZ.2024.3516132","url":null,"abstract":"The issue of sliding flexible prescribed performance control (SFPPC) of input saturated nonlinear systems (ISNSs) is first studied in this article. Compared to the traditional PPC and the finite-time PPC algorithms for ISNSs, under which the performance constraint boundaries (PCBs) present the symmetrical or asymmetric “horn” shape, which leads to a large jitter in the tracking error before the system reaches steady state; and once the parameters are selected, the PCBs are fixed, when the initial state (or reference signal) changes, it is necessary to reverify whether the initial error still satisfies the initial constraint condition. By designing a new pair of sliding flexible PCBs (SFPCBs) associated with the initial error, a novel SFPPC algorithm is presented in this article, which presents two main advantages: 1) the SFPCBs can slide adaptively with the initial tracking error without increasing the measure of the initial PCBs, implying that the proposed SFPPC algorithm can be applied to ISNSs with arbitrary initial errors without sacrificing the initial control performance; 2) the proposed SFPPC algorithm achieves a tradeoff between performance constraint and input saturation, i.e., the SFPCBs can adaptively increase when the control input exceeds the maximum allowable threshold, effectively avoiding singularity, and when the control input is within the saturation threshold range, the SFPCBs can adaptively revert back to the original PCBs. The results demonstrate that the proposed SFPPC approach can guarantee that the system output tracks the desired signal, and the tracking error always kept within the SFPCBs that depend on initial error, input, and output constraints. The developed algorithm is exemplified by means of simulation instances.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1241-1252"},"PeriodicalIF":10.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815558","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