Ieee-Caa Journal of Automatica Sinica最新文献_第6页

Privacy Distributed Constrained Optimization Over Time-Varying Unbalanced Networks and Its Application in Federated Learning

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124869

Mengli Wei;Wenwu Yu;Duxin Chen;Mingyu Kang;Guang Cheng

{"title":"Privacy Distributed Constrained Optimization Over Time-Varying Unbalanced Networks and Its Application in Federated Learning","authors":"Mengli Wei;Wenwu Yu;Duxin Chen;Mingyu Kang;Guang Cheng","doi":"10.1109/JAS.2024.124869","DOIUrl":"https://doi.org/10.1109/JAS.2024.124869","url":null,"abstract":"This paper investigates a class of constrained distributed zeroth-order optimization (ZOO) problems over time-varying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs. We hereby propose a novel algorithm, termed the differential privacy (DP) distributed push-sum based zeroth-order constrained optimization algorithm (DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs, offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm (ZOCOA-FL) to address challenges stemming from the time-varying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares (DLS) and decentralized federated learning (DFL) tasks.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 2","pages":"335-346"},"PeriodicalIF":15.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106682","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 Correntropy-Based Echo State Network with Application to Time Series Prediction

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124932

Xiufang Chen;Zhenming Su;Long Jin;Shuai Li

{"title":"A Correntropy-Based Echo State Network with Application to Time Series Prediction","authors":"Xiufang Chen;Zhenming Su;Long Jin;Shuai Li","doi":"10.1109/JAS.2024.124932","DOIUrl":"https://doi.org/10.1109/JAS.2024.124932","url":null,"abstract":"As a category of recurrent neural networks, echo state networks (ESNs) have been the topic of in-depth investigations and extensive applications in a diverse array of fields, with spectacular triumphs achieved. Nevertheless, the traditional ESN and the majority of its variants are devised in the light of the second-order statistical information of data (e.g., variance and covariance), while more information is neglected. In the context of information theoretic learning, correntropy demonstrates the capacity to grab more information from data. Therefore, under the guidelines of the maximum correntropy criterion, this paper proposes a correntropy-based echo state network (CESN) in which the first-order and higher-order information of data is captured, promoting robustness to noise. Furthermore, an incremental learning algorithm for the CESN is presented, which has the expertise to update the CESN when new data arrives, eliminating the need to retrain the network from scratch. Finally, experiments on benchmark problems and comparisons with existing works are provided to verify the effectiveness and superiority of the proposed CESN.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 2","pages":"425-435"},"PeriodicalIF":15.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106670","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 Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124962

Tianyu Wang;Fan Zhou;Yangjie Wu;Jun Zhao;Wei Wang

{"title":"A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics","authors":"Tianyu Wang;Fan Zhou;Yangjie Wu;Jun Zhao;Wei Wang","doi":"10.1109/JAS.2024.124962","DOIUrl":"https://doi.org/10.1109/JAS.2024.124962","url":null,"abstract":"As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status (mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a central-wise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 2","pages":"369-380"},"PeriodicalIF":15.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106675","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

Constrained Networked Predictive Control for Nonlinear Systems Using a High-Order Fully Actuated System Approach

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124764

Yi Huang;Guo-Ping Liu;Yi Yu;Wenshan Hu

引用次数: 0

Exponential Stability of Impulsive System via Saturated Sliding Mode Control

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124734

Miaomiao Yu;Xiaodi Li

引用次数: 0

Dynamic Event-Triggered Active Disturbance Rejection Formation Control for Constrained Underactuated AUVs

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124617

Zhiguang Feng;Sibo Yao

引用次数: 0

When Software Security Meets Large Language Models: A Survey

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124971

Xiaogang Zhu;Wei Zhou;Qing-Long Han;Wanlun Ma;Sheng Wen;Yang Xiang

引用次数: 0

Improved Zero-Dynamics Attack Scheduling With State Estimation

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124737

Zhe Wang;Heng Zhang;Chaoqun Yang;Xianghui Cao

引用次数: 0

Finite-Time Stability of Impulsive and Switched Hybrid Systems With Delay-Dependent Impulses

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124758

Taixiang Zhang;Jinde Cao;Mahmoud Abdel-Aty;Ardak Kashkynbayev

引用次数: 0

Broad-Learning-System-Based Model-Free Adaptive Predictive Control for Nonlinear MASs Under DoS Attacks

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-20 DOI: 10.1109/JAS.2024.124929

Hongxing Xiong;Guangdeng Chen;Hongru Ren;Hongyi Li

{"title":"Broad-Learning-System-Based Model-Free Adaptive Predictive Control for Nonlinear MASs Under DoS Attacks","authors":"Hongxing Xiong;Guangdeng Chen;Hongru Ren;Hongyi Li","doi":"10.1109/JAS.2024.124929","DOIUrl":"https://doi.org/10.1109/JAS.2024.124929","url":null,"abstract":"In this paper, the containment control problem in nonlinear multi-agent systems (NMASs) under denial-of-service (DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent lin-earization model of NMASs. Then, a novel model-free adaptive predictive control (MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 2","pages":"381-393"},"PeriodicalIF":15.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106673","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