Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455655
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Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455691
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Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455653
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Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455689
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Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455649
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Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455651
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Pub Date : 2024-09-17DOI: 10.1109/TSMC.2024.3455693
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Pub Date : 2024-09-05DOI: 10.1109/TSMC.2024.3450601
Giuseppe Franzè;Francesco Giannini;Vicenç Puig;Giancarlo Fortino
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.
{"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}
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.
{"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}
Pub Date : 2024-09-05DOI: 10.1109/TSMC.2024.3448226
Le Cheng;Peican Zhu;Chao Gao;Zhen Wang;Xuelong Li
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.
社交网络的快速发展给谣言提供了扰乱社会秩序的机会。然而，由于用户和信息传播动态的多样性和复杂性，社交网络中谣言源的定位仍然是一个有待很好解决的关键问题。近年来，虽然有多种方法被提出来试图解决这一问题，但它们都存在准确性和模型复杂性之间的矛盾。为了有效地检测信息源，本文提出了一种通过图卷积网络在社交网络中进行信息源检测（HFSD）的启发式框架，该框架可应对三大挑战，包括1) 用户和信息传播动态的多样性和复杂性；2) 检测多个信息源的难度，尤其是在不知道信息源数量的情况下；3) 由于信息源和非信息源的样本量差异较大而导致的类不平衡问题。具体来说，首先，为了抵消用户和信息的多样性和复杂性，我们在原始特征向量中编码了不同种类的用户和信息特征；其次，为了解决多源检测问题，我们在模型的最后一层采用了二元分类法，这不同于通常应用于单源场景的 n 分类法；最后，为了解决类不平衡问题，我们设计了一种平衡机制，可以抵消源和非源集合之间样本量的差异。在 12 个实际数据集上进行的大量实验证明，HFSD 可以处理上述问题，而且性能明显优于最先进的算法。
{"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}
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