IEEE Transactions on Cybernetics最新文献_第9页

Multigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection 基于z熵不确定度的多粒度数据分析高效鲁棒特征选择

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-12-03 DOI: 10.1109/TCYB.2024.3499952

Kehua Yuan;Duoqian Miao;Witold Pedrycz;Hongyun Zhang;Liang Hu

{"title":"Multigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection","authors":"Kehua Yuan;Duoqian Miao;Witold Pedrycz;Hongyun Zhang;Liang Hu","doi":"10.1109/TCYB.2024.3499952","DOIUrl":"10.1109/TCYB.2024.3499952","url":null,"abstract":"Multigranularity data analysis has recently become an active research topic in the intelligent computing and data mining fields. Feature selection via multigranularity data analysis is an effective tool for characterizing hierarchical data and enhancing the accuracy of the results. Although the multigranularity data analysis method has been widely adopted for feature selection, existing studies still present one prevalent disadvantage: multigranularity data analysis mostly focuses on information presented at a single granularity while ignoring the hierarchical structure of multigranularity data, which is contrary to the nature of multigranularity. Hence, this article proposes a multigranularity data analysis with a zentropy uncertainty measure for efficient and robust feature selection. Specifically, a consistent degree is first introduced to obtain optimal granularity combinations and establish an efficient neighborhood model for multigranularity information processing. Then, a novel and robust uncertainty measure is developed by integrating the multigranularity information, namely the zentropy-based measure. Considering its accuracy among uncertainty measures, two important measures are further designed and applied to feature selection. Extensive experiments demonstrate that the proposed method can achieve better robustness and classification performance than other state-of-the-art methods.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"740-752"},"PeriodicalIF":9.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776397","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

Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance 二次局部非对称随机非线性系统的随机神经网络控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-12-03 DOI: 10.1109/TCYB.2024.3502496

Yu Xia;Ke Xiao;Jinde Cao;Radu-Emil Precup;Yogendra Arya;Hak-Keung Lam;Leszek Rutkowski

{"title":"Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance","authors":"Yu Xia;Ke Xiao;Jinde Cao;Radu-Emil Precup;Yogendra Arya;Hak-Keung Lam;Leszek Rutkowski","doi":"10.1109/TCYB.2024.3502496","DOIUrl":"10.1109/TCYB.2024.3502496","url":null,"abstract":"This article presents an adaptive neural network control scheme with prescribed performance for stochastic nonlinear systems. Unlike existing adaptive stochastic control schemes that primarily utilize deterministic neural networks for approximations in complex stochastic environments, we employ stochastic neural networks to approximate the stochastic nonlinear terms, effectively resolving the “memory overflow” issue. Moreover, we propose a novel prescribed performance design method, which distinguishes itself from the previous prescribed performance control schemes by integrating a quadratic characteristic capable of suppressing transient input vibrations, along with a local asymmetric characteristic that optimize both transient output overshoot and steady-state error bias. Furthermore, the proposed control scheme is implemented within a fixed-time framework to ensure that all closed-loop systems are fixed-time bounded in probability, with the tracking error consistently within the predefined performance bounds. Simulation results validate the effectiveness of the proposed control scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"867-879"},"PeriodicalIF":9.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777254","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

Dynamic-Memory Protocol-Based Synchronization for Semi-Markov Jump Reaction-Diffusion CDNs 基于动态内存协议的半马尔可夫跳变扩散cdn同步

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-12-03 DOI: 10.1109/TCYB.2024.3502684

Wenhai Qi;Zhenzhen Yuan;Guangdeng Zong;Jinde Cao;Huaicheng Yan;Jun Cheng;Shan Jin

引用次数: 0

Optimal Stealthy Attack With Side Information Against Remote State Estimation: A Corrupted Innovation-Based Strategy 针对远程状态估计的带侧信息的最优隐身攻击：一种基于腐败创新的策略

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-12-03 DOI: 10.1109/TCYB.2024.3502790

Li-Wei Mao;Guang-Hong Yang

引用次数: 0

Natural Modal Sketching Network: An Interpretable Approach for Bearing Impulsive Feature Extraction 自然模态素描网络：一种可解释的轴承脉冲特征提取方法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-12-03 DOI: 10.1109/TCYB.2024.3497597

Yuan Zheng;Weihua Li;Guolin He;Kang Ding;Zhuyun Chen

{"title":"Natural Modal Sketching Network: An Interpretable Approach for Bearing Impulsive Feature Extraction","authors":"Yuan Zheng;Weihua Li;Guolin He;Kang Ding;Zhuyun Chen","doi":"10.1109/TCYB.2024.3497597","DOIUrl":"10.1109/TCYB.2024.3497597","url":null,"abstract":"Impulsive feature (IF) response is an essential indicator for rolling bearing fault. However, it is overwhelmed by strong noise and difficult to extract in real scenes. Although deep learning-based methods are powerful in feature extraction, their logic and extracting principles possess weak interpretability and credibility. Their further implementation is hampered. In this article, a natural modal sketching network (NMSNet) is constructed to achieve robust and credible bearing IF extraction. First, the modal response is designed as a convolutional kernel of NMSNet, and the forward propagation logic is interpreted as natural modal sketching, including modal response recovery and weighted superposition. The logic derives from the fault mechanism and brings solid credibility to NMSNet. Second, a novel correction algorithm is developed to interpret the extraction principle of NMSNet in theory and achieve noise elimination due to its filter nature. Third, NMSNet realizes adaptive modal sketching via the formulated weighted fusion strategy and training constraint. Finally, simulation and experiment have been carried out to verify the effectiveness and noise robustness of NMSNet. The fault-related interpretability analysis confirms the knowledge acquisition of NMSNet, which strengthens the credibility of IF extraction.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"953-968"},"PeriodicalIF":9.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776725","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 SYSTEMS, MAN, AND CYBERNETICS SOCIETY Ieee系统，人和控制论学会

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-27 DOI: 10.1109/TCYB.2024.3499293

引用次数: 0

IEEE Transactions on Cybernetics 电气和电子工程师学会控制论论文集

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-27 DOI: 10.1109/TCYB.2024.3499297

引用次数: 0

Convolutional- and Deep Learning-Based Techniques for Time Series Ordinal Classification 基于卷积和深度学习的时间序列有序分类技术

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-27 DOI: 10.1109/TCYB.2024.3498100

Rafael Ayllón-Gavilán;David Guijo-Rubio;Pedro Antonio Gutiérrez;Anthony Bagnall;César Hervás-Martínez

{"title":"Convolutional- and Deep Learning-Based Techniques for Time Series Ordinal Classification","authors":"Rafael Ayllón-Gavilán;David Guijo-Rubio;Pedro Antonio Gutiérrez;Anthony Bagnall;César Hervás-Martínez","doi":"10.1109/TCYB.2024.3498100","DOIUrl":"10.1109/TCYB.2024.3498100","url":null,"abstract":"Time-series classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time-series ordinal classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time-series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this article presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state of the art. Both convolutional- and deep-learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this article contributes to the establishment of the state of the art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"537-549"},"PeriodicalIF":9.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Foundation - Reflecting on 50 Years of Impact IEEE 基金会 - 反思 50 年来的影响

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-27 DOI: 10.1109/TCYB.2024.3507252

引用次数: 0

Correction for “Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control” 对 "通过基于努斯鲍姆的混合控制，在具有混合未知控制方向的大功率多代理系统中达成共识 "的更正

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-27 DOI: 10.1109/TCYB.2020.3045819

Maolong Lv;Wenwu Yu;Jinde Cao;Simone Baldi

引用次数: 0