In this article, the evolution of social power is studied within a unified framework comprising two classes of individuals: oblivious individuals and stubborn individuals, whose opinion dynamics are described by the DeGroot averaging model and the Friedkin-Johnsen model, respectively. A proper subset of the simplex is identified to ensure the well-posedness of social power, and it is demonstrated that the corresponding opinion dynamics is convergent for each issue by restricting the initial social power to this proper subset. Through the reflected appraisal mechanism, a nonlinear mapping governing the social power evolution together with its invariant set is derived, and some sufficient conditions with linear time complexity for the convergence of social power are established by proving that this nonlinear mapping is contractive on the invariant set. Furthermore, for the final social power, it is found that both autocratic and democratic social power cannot be achieved during the evolution, and the average social power of oblivious individuals is larger than that of stubborn individuals, indicating that the network topology has a greater impact on social power than individual stubbornness. In addition, it is observed that the final social power ranking of oblivious individuals is consistent with their centrality ranking, and a rigorous lower bound on the final social power is derived for each stubborn individual. Finally, a numerical example is provided to demonstrate the correctness of the theoretical analysis.
{"title":"Social Power Evolution Analysis for Friedkin-Johnsen Model With Oblivious Individuals.","authors":"Hong-Xiang Hu,Guanghui Wen,Yun Chen,Fan Zhang,Tingwen Huang","doi":"10.1109/tcyb.2025.3635531","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3635531","url":null,"abstract":"In this article, the evolution of social power is studied within a unified framework comprising two classes of individuals: oblivious individuals and stubborn individuals, whose opinion dynamics are described by the DeGroot averaging model and the Friedkin-Johnsen model, respectively. A proper subset of the simplex is identified to ensure the well-posedness of social power, and it is demonstrated that the corresponding opinion dynamics is convergent for each issue by restricting the initial social power to this proper subset. Through the reflected appraisal mechanism, a nonlinear mapping governing the social power evolution together with its invariant set is derived, and some sufficient conditions with linear time complexity for the convergence of social power are established by proving that this nonlinear mapping is contractive on the invariant set. Furthermore, for the final social power, it is found that both autocratic and democratic social power cannot be achieved during the evolution, and the average social power of oblivious individuals is larger than that of stubborn individuals, indicating that the network topology has a greater impact on social power than individual stubbornness. In addition, it is observed that the final social power ranking of oblivious individuals is consistent with their centrality ranking, and a rigorous lower bound on the final social power is derived for each stubborn individual. Finally, a numerical example is provided to demonstrate the correctness of the theoretical analysis.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"10 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717358","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 : 2025-12-09DOI: 10.1109/tcyb.2025.3637764
Chayan Banerjee,Zhiyong Chen,Nasimul Noman
Actor-critic (AC) algorithms are model-free deep reinforcement learning techniques that have consistently demonstrated effectiveness across various domains. Enhancing exploration (action entropy) and exploitation (expected return) through more efficient sample utilization is pivotal to their success. A key strategy for a learning algorithm is to intelligently navigate the environment's state space, prioritizing the exploration of rarely visited states over frequently encountered ones. However, conventional approaches rarely quantify a novel state's utility for policy learning, which can lead to inefficient exploration. To address this, we propose an innovative approach to bolster exploration by employing an intrinsic reward based on a state's novelty and the potential benefits of exploring that state, which we term plausible novelty. Our method seamlessly integrates with off-policy AC algorithms. By incentivizing the exploration of plausibly novel states, AC algorithms can achieve substantial improvements in sample efficiency and overall training performance. Empirical results demonstrate 19% improvement in training return and 30% reduction in standard deviation, averaged across comparisons of three benchmark algorithm pairs in five different environments.
{"title":"Enhancing Exploration in Actor-Critic Algorithms: An Approach to Incentivize Plausible Novel States.","authors":"Chayan Banerjee,Zhiyong Chen,Nasimul Noman","doi":"10.1109/tcyb.2025.3637764","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3637764","url":null,"abstract":"Actor-critic (AC) algorithms are model-free deep reinforcement learning techniques that have consistently demonstrated effectiveness across various domains. Enhancing exploration (action entropy) and exploitation (expected return) through more efficient sample utilization is pivotal to their success. A key strategy for a learning algorithm is to intelligently navigate the environment's state space, prioritizing the exploration of rarely visited states over frequently encountered ones. However, conventional approaches rarely quantify a novel state's utility for policy learning, which can lead to inefficient exploration. To address this, we propose an innovative approach to bolster exploration by employing an intrinsic reward based on a state's novelty and the potential benefits of exploring that state, which we term plausible novelty. Our method seamlessly integrates with off-policy AC algorithms. By incentivizing the exploration of plausibly novel states, AC algorithms can achieve substantial improvements in sample efficiency and overall training performance. Empirical results demonstrate 19% improvement in training return and 30% reduction in standard deviation, averaged across comparisons of three benchmark algorithm pairs in five different environments.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"133 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710813","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 : 2025-12-08DOI: 10.1109/tcyb.2025.3632756
Lizhang Wang, Zidong Wang, Qinyuan Liu
{"title":"Hybrid-Driven State Estimation With Adaptive Cross-Coupled Priors: Enhancing Data Representation and Model Robustness","authors":"Lizhang Wang, Zidong Wang, Qinyuan Liu","doi":"10.1109/tcyb.2025.3632756","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3632756","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"3 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704123","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 : 2025-12-08DOI: 10.1109/tcyb.2025.3638350
Yue Zhang, Yan-Wu Wang, Xiao-Kang Liu, Zhi-Wei Liu
{"title":"On the Design of Optimal Consensus With Deception-Eliminating Scheme and Asynchronous Updates","authors":"Yue Zhang, Yan-Wu Wang, Xiao-Kang Liu, Zhi-Wei Liu","doi":"10.1109/tcyb.2025.3638350","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3638350","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"14 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704122","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 : 2025-12-04DOI: 10.1109/tcyb.2025.3637383
Yafeng Li, Bin Du, Changchun Hua, Guopin Liu, Yu Zhang
{"title":"Fully Distributed Fault-Tolerant Consensus-Tracking Control for Multiple Wheeled Mobile Robots With Event-Triggered Communication","authors":"Yafeng Li, Bin Du, Changchun Hua, Guopin Liu, Yu Zhang","doi":"10.1109/tcyb.2025.3637383","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3637383","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674228","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 : 2025-12-04DOI: 10.1109/tcyb.2025.3637910
Jie Su, Yongduan Song
{"title":"Adaptive Prescribed-Time Control of Uncertain Self-Restructuring Nonaffine Nonlinear Systems","authors":"Jie Su, Yongduan Song","doi":"10.1109/tcyb.2025.3637910","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3637910","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"27 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674234","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 : 2025-12-03DOI: 10.1109/tcyb.2025.3625390
Zepeng Ning,Wei Xing Zheng,Xunyuan Yin
This article treats the problems of the stability, boundedness, and stabilizing control of discrete-time semi-Markov jump systems (SMJSs) with fragmentary semi-Markov kernel (SMK) under persistent disturbances. Since the statistical characteristics of stochastic processes are difficult to describe precisely and comprehensively, the available SMK information may be fragmentary, and only a portion of the information is known. Regarding this problem, we propose new approaches that leverage all the known SMK information and derive new criteria for analysis and control. The feasibility therein can be enhanced compared to the existing approaches with inadequate utilization of the known SMK information. Additionally, a polytopic approach is proposed to approximate the unknown portion of the SMK information to enrich the information available for subsequent analysis and control design. This is achieved through constructing a polytopic quadratic Lyapunov-like function (LF), which further improves the feasibility. In this way, both the available information and the approximated unknown part about the SMK are incorporated. Meanwhile, the ultimate boundedness of the closed-loop semi-Markov jump linear system (SMJLS) is ensured in the mean-square sense without requiring the deviation between the state and its nominal one to converge at all times. We illustrate the validity and superiority of the proposed approach through a numerical example and a simulated chemical process example using a machine learning-based surrogate model.
{"title":"Analysis and Control of Semi-Markov Jump Linear Systems Under Persistent Disturbances via Full Utilization of Fragmentary Kernel.","authors":"Zepeng Ning,Wei Xing Zheng,Xunyuan Yin","doi":"10.1109/tcyb.2025.3625390","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3625390","url":null,"abstract":"This article treats the problems of the stability, boundedness, and stabilizing control of discrete-time semi-Markov jump systems (SMJSs) with fragmentary semi-Markov kernel (SMK) under persistent disturbances. Since the statistical characteristics of stochastic processes are difficult to describe precisely and comprehensively, the available SMK information may be fragmentary, and only a portion of the information is known. Regarding this problem, we propose new approaches that leverage all the known SMK information and derive new criteria for analysis and control. The feasibility therein can be enhanced compared to the existing approaches with inadequate utilization of the known SMK information. Additionally, a polytopic approach is proposed to approximate the unknown portion of the SMK information to enrich the information available for subsequent analysis and control design. This is achieved through constructing a polytopic quadratic Lyapunov-like function (LF), which further improves the feasibility. In this way, both the available information and the approximated unknown part about the SMK are incorporated. Meanwhile, the ultimate boundedness of the closed-loop semi-Markov jump linear system (SMJLS) is ensured in the mean-square sense without requiring the deviation between the state and its nominal one to converge at all times. We illustrate the validity and superiority of the proposed approach through a numerical example and a simulated chemical process example using a machine learning-based surrogate model.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"127 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664263","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 : 2025-12-02DOI: 10.1109/tcyb.2025.3635299
Yanwen Liu,Zhengda Ma,Jie Ding,Xiang Li
This article focuses on the constrained maximal controllability of complex networks, which aims to maximize the generic dimension of controllable subspace of networks with a given candidate set of constrained input locations. To address this issue, we first transform it to a maximum general-cactus cover problem. By introducing network flow, this problem is further converted to a minimum-cost maximum-flow problem. An algorithm named minimum-cost maximum-flow-based general-cactus cover (MMGC) is proposed to achieve the optimal solution. Furthermore, a series of simulations on Erdős-Rényi networks (ERNs) and scale-free networks (SFNs) and applications in network controllability robustness demonstrates the effectiveness of MMGC. The simulation results have revealed that augmenting the number or range of inputs can enhance the controllability of networks, and the presence of multicyclic structures significantly strengthens the controllability robustness of complex networks.
{"title":"Constrained Maximal Controllability of Complex Networks.","authors":"Yanwen Liu,Zhengda Ma,Jie Ding,Xiang Li","doi":"10.1109/tcyb.2025.3635299","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3635299","url":null,"abstract":"This article focuses on the constrained maximal controllability of complex networks, which aims to maximize the generic dimension of controllable subspace of networks with a given candidate set of constrained input locations. To address this issue, we first transform it to a maximum general-cactus cover problem. By introducing network flow, this problem is further converted to a minimum-cost maximum-flow problem. An algorithm named minimum-cost maximum-flow-based general-cactus cover (MMGC) is proposed to achieve the optimal solution. Furthermore, a series of simulations on Erdős-Rényi networks (ERNs) and scale-free networks (SFNs) and applications in network controllability robustness demonstrates the effectiveness of MMGC. The simulation results have revealed that augmenting the number or range of inputs can enhance the controllability of networks, and the presence of multicyclic structures significantly strengthens the controllability robustness of complex networks.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"72 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657040","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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