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Together, we are advancing technology 我们共同推动技术进步

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455655

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IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455691

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IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455653

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IEEE Transactions on Systems, Man, and Cybernetics publication information 电气和电子工程师学会《系统、人和控制论》期刊出版信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455689

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IEEE Transactions on Systems, Man, and Cybernetics publication information 电气和电子工程师学会《系统、人和控制论》期刊出版信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455649

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IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455651

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IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-17 DOI: 10.1109/TSMC.2024.3455693

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Embedding the State Trajectories of Nonlinear Systems via Multimodel Linear Descriptions: A Data-Driven-Based Algorithm 通过多模型线性描述嵌入非线性系统的状态轨迹：基于数据驱动的算法

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-05 DOI: 10.1109/TSMC.2024.3450601

Giuseppe Franzè;Francesco Giannini;Vicenç Puig;Giancarlo Fortino

{"title":"Embedding the State Trajectories of Nonlinear Systems via Multimodel Linear Descriptions: A Data-Driven-Based Algorithm","authors":"Giuseppe Franzè;Francesco Giannini;Vicenç Puig;Giancarlo Fortino","doi":"10.1109/TSMC.2024.3450601","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3450601","url":null,"abstract":"In this article, the problem of generating multimodel state space descriptions in a data-driven context to embed the dynamic behavior of nonlinear systems is addressed. The proposed methodology takes advantage of three ingredients: 1) linear time-invariant system behavior; 2) data-driven modeling; and 3) reinforcement learning (RL) technicalities. These elements are properly combined to develop a data-driven algorithm capable to derive an accurate outer convex approximation of the nonlinear evolution. In particular, an actor-critic RL scheme is designed to efficiently comply with the exhaustive research on the whole parameter space. At each iteration, the effectiveness of the obtained uncertain polytopic model is tested by a probabilistic approach based on a confidence level metrics. As the main merits of the proposed approach are concerned, the following aspect clearly stands up: the development of an interdisciplinary methodology that takes advantage of system theory, probabilistic arguments and RL capabilities giving rise to an harmonized architecture in charge to deal with a vast class of nonlinear systems. Finally, the validity of the proposed approach is tested by resorting to benchmark examples that allow to quantify the level of accuracy of the computed convex hull.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450983","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}

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High-Precision Quick Control in Multivariable Time-Varying Nonlinear System: A Biological Decision Model Predictive Control Algorithm 多变量时变非线性系统中的高精度快速控制：生物决策模型预测控制算法

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-05 DOI: 10.1109/TSMC.2024.3449332

Jinying Yang;Yongjun Zhang;Qiang Guo;Xiong Xiao;Tanju Yildirim;Fei Zhang

{"title":"High-Precision Quick Control in Multivariable Time-Varying Nonlinear System: A Biological Decision Model Predictive Control Algorithm","authors":"Jinying Yang;Yongjun Zhang;Qiang Guo;Xiong Xiao;Tanju Yildirim;Fei Zhang","doi":"10.1109/TSMC.2024.3449332","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3449332","url":null,"abstract":"To solve the problem of unsatisfactory control and poor real-time performance of nonlinear time-varying multi-input systems, this article proposes an intelligent model predictive control (MPC) algorithm inspired by heuristic dynamic programming (HDP), biological control theory, and operations research. Considering that the internal feedback information from a neural network (NN) is low, a multilevel feedback NN is proposed. Combining an NN with a biofeedback mechanism increases the internal feedback information and improves the convergence accuracy of the NN. The multilevel feedback network is used in three internal networks of the intelligent MPC algorithm. In order to improve the convergence speed of the proposed algorithm, a biologically inspired central coordination module and operations research theory inspired priority factor module is incorporated within the HDP algorithm. The prediction accuracy and control speed of the algorithm for nonlinear time-varying systems is greatly improved without affecting the control accuracy. The stability and convergence of the intelligent MPC algorithm is demonstrated on test data. Finally, the effectiveness and superiority of the proposed MPC algorithm is verified and compared against several traditional algorithms.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443053","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}

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A Heuristic Framework for Sources Detection in Social Networks via Graph Convolutional Networks 通过图卷积网络检测社交网络中来源的启发式框架

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-09-05 DOI: 10.1109/TSMC.2024.3448226

Le Cheng;Peican Zhu;Chao Gao;Zhen Wang;Xuelong Li

{"title":"A Heuristic Framework for Sources Detection in Social Networks via Graph Convolutional Networks","authors":"Le Cheng;Peican Zhu;Chao Gao;Zhen Wang;Xuelong Li","doi":"10.1109/TSMC.2024.3448226","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3448226","url":null,"abstract":"The rapid development of social networks has given opportunities for rumors to disturb the order of society. However, due to the diversity and complexity of users and information dissemination dynamics, localizing the rumor sources in social networks is still a critical and crucial problem yet to be well solved. Recent years, although several methods have been proposed to attempt to solve this problem, they suffer from the contradiction between accuracy and model complexity. To detect information sources efficiently, this article propose a heuristic framework for sources detection (HFSD) in social networks via graph convolutional networks which handles three major challenges, including: 1) the diversity and complexity of users and information dissemination dynamics; 2) difficulty to detect multiple sources, especially without knowing the number of sources; and 3) the class imbalance problem caused by the large differences in sample size of sources and nonsources. Specifically, first, to counteract the diversity and complexity of users and information, different kinds of features of users and information are encoded in the raw feature vectors; second, to address multisource detection, we adopt a binary classification in the last layer of model, which is different from the n-classification methods that are always applied to single-source scenario; finally, to solve the class imbalance problem, we design a balance mechanism which offsets the differences in sample size between the sets of sources and nonsources. Extensive experiments conducted on 12 real-world datasets demonstrate that HFSD can handle problems mentioned above and outperforms than state-of-the-art algorithms significantly.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442927","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