IEEE Transactions on Cybernetics最新文献_第4页

A Scalable Test Problem Generator for Sequential Transfer Optimization.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-20 DOI: 10.1109/TCYB.2025.3547565

Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan

{"title":"A Scalable Test Problem Generator for Sequential Transfer Optimization.","authors":"Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan","doi":"10.1109/TCYB.2025.3547565","DOIUrl":"10.1109/TCYB.2025.3547565","url":null,"abstract":"Despite the increasing interest in sequential transfer optimization (STO), a comprehensive benchmark suite for systematically comparing various STO algorithms remains underexplored. Existing test problems, which are often manually configured and lack scalability, can result in biased and nongeneralizable algorithm performance. In light of the above, we first introduce four concepts for characterizing STO problems (STOPs) in this study and present an important feature, namely similarity distribution, to quantitatively delineate the relationship between the optimal solutions of source and target tasks. Subsequently, we present general design guidelines for STOPs and introduce a problem generator that demonstrates strong scalability. Specifically, the similarity distribution of a problem can be easily customized through a novel inverse generation strategy, allowing for a continuous spectrum that captures the diverse similarity relationships present in real-world scenarios. Lastly, a benchmark suite comprising 12 STOPs, characterized by a range of customized similarity relationships, has been developed using the proposed generator and will serve as a platform for examining various STO algorithms. For instance, biased transferability representation, irregular mapping learning behaviors, and performance improvements unrelated to search experience are significant empirical findings that previous benchmarks failed to reveal, yet can be effectively identified through our test problems. The source code of the proposed problem generator is available at https://github.com/XmingHsueh/STOP-G.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669711","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 State Space Model for Multiobject Full 3-D Information Estimation From RGB-D Images

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/tcyb.2025.3548788

Jiaming Zhou, Qing Zhu, Yaonan Wang, Mingtao Feng, Jian Liu, Jianan Huang, Ajmal Mian

引用次数: 0

A Subspace Search-Based Evolutionary Algorithm for Large-Scale Constrained Multiobjective Optimization and Application

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/tcyb.2025.3548414

Xuanxuan Ban, Jing Liang, Kunjie Yu, Kangjia Qiao, Ponnuthurai Nagaratnam Suganthan, Yaonan Wang

引用次数: 0

Unsupervised Feature Selection for High-Order Embedding Learning and Sparse Learning

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/tcyb.2025.3546658

Zebiao Hu, Jian Wang, Jacek Mańdziuk, Zhongxin Ren, Nikhil R. Pal

引用次数: 0

An Improved Topology Identification Method of Complex Dynamical Networks

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/tcyb.2025.3547772

Yi Zheng, Xiaoqun Wu, Ziye Fan, Kebin Chen, Jinhu Lü

引用次数: 0

Feasible Policy Iteration With Guaranteed Safe Exploration.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3542223

Yuhang Zhang, Yujie Yang, Shengbo Eben Li, Yao Lyu, Jingliang Duan, Zhilong Zheng, Dezhao Zhang

{"title":"Feasible Policy Iteration With Guaranteed Safe Exploration.","authors":"Yuhang Zhang, Yujie Yang, Shengbo Eben Li, Yao Lyu, Jingliang Duan, Zhilong Zheng, Dezhao Zhang","doi":"10.1109/TCYB.2025.3542223","DOIUrl":"10.1109/TCYB.2025.3542223","url":null,"abstract":"Safety guarantee is an important topic when training real-world tasks with reinforcement learning (RL). During online environmental exploration, any constraint violation can lead to significant property damage and risks to personnel. Existing safe RL methods either exclusively address safety concerns after reaching optimality or incorporate a certain degree of tolerance for constraint violations during training. This article proposes a feasible policy iteration framework that can guarantee absolute safety during online exploration, i.e., constraint violations never happen in real-world interactions. The key to maintaining absolute safety lies in confining the environmental exploration at each step always within the feasible region of the current policy. This feasible region is described by a newly defined constraint decay function with uncertainty, ensuring the forward invariance of the feasible region under the worst case. Within the proposed framework, the feasible region maintains its monotonic expanding property and converges to its maximum extent, even though only local samples are available, i.e., the agent only has access to samples within the feasible region. Meanwhile, the trained policy also improves monotonically within its corresponding feasible region if one can use different updating rules inside and outside the feasible region. Finally, practical algorithms are designed with the actor-critic-scenery architecture, consisting of three modules: 1) safe exploration; 2) model error estimation; and 3) network update. Experimental results indicate that our algorithms achieve performance comparable to baselines while maintaining zero constraint violation throughout the entire training process. In contrast, the baseline algorithm typically requires thousands of constraint violations to achieve the same performance. These findings suggest a substantial potential for applying feasible policy iteration in real-world tasks, enabling the online evolution of intricate systems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657070","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

Predictor-Based Feedback Control for Discrete-Time Time-Variant Linear State-Delayed Systems With Distinct Input Delays via State Transition Matrices.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3542998

Ai-Guo Wu, Jie Zhang, Shi-Long Shen

引用次数: 0

Finite-Time Stabilizers for Large-Scale Stochastic Boolean Networks.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3545689

Lin Lin, James Lam, Wai-Ki Ching, Qian Qiu, Liangjie Sun, Bo Min

{"title":"Finite-Time Stabilizers for Large-Scale Stochastic Boolean Networks.","authors":"Lin Lin, James Lam, Wai-Ki Ching, Qian Qiu, Liangjie Sun, Bo Min","doi":"10.1109/TCYB.2025.3545689","DOIUrl":"10.1109/TCYB.2025.3545689","url":null,"abstract":"This article presents a distributed pinning control strategy aimed at achieving global stabilization of Markovian jump Boolean control networks. The strategy relies on network matrix information to choose controlled nodes and adopts the algebraic state space representation approach for designing pinning controllers. Initially, a sufficient criterion is established to verify the global stability of a given Markovian jump Boolean network (MJBN) with probability one at a specific state within finite time. To stabilize an unstable MJBN at a predetermined state, the selection of pinned nodes involves removing the minimal number of entries, ensuring that the network matrix transforms into a strictly lower (or upper) triangular form. For each pinned node, two types of state feedback controllers are developed: 1) mode-dependent and 2) mode-independent, with a focus on designing a minimally updating controller. The choice of controller type is determined by the feasibility condition of the mode-dependent pinning controller, which is articulated through the solvability of matrix equations. Finally, the theoretical results are illustrated by studying the T cell large granular lymphocyte survival signaling network consisting of 54 genes and 6 stimuli.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657078","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

Resilient Distributed Nash Equilibrium Control for Nonlinear MASs Under DoS Attacks.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3543675

Shihan Zhou, Chao Deng, Sha Fan, Bohui Wang, Wei-Wei Che

{"title":"Resilient Distributed Nash Equilibrium Control for Nonlinear MASs Under DoS Attacks.","authors":"Shihan Zhou, Chao Deng, Sha Fan, Bohui Wang, Wei-Wei Che","doi":"10.1109/TCYB.2025.3543675","DOIUrl":"10.1109/TCYB.2025.3543675","url":null,"abstract":"This article investigates the resilient distributed Nash equilibrium (NE) control problem for nonlinear multiagent systems (MASs) that suffers from denial-of-service (DoS) attacks in the communication network. Different from the existing works on NE seeking in noncooperative games, it is the first trial to consider the resilient distributed NE control problem for nonlinear MASs under DoS attacks. To overcome the challenges caused by the considered problem, a new layered NE control method is developed, which consists of a resilient distributed NE seeking algorithm, two-stage cascade filters, and a resilient adaptive controller. Specifically, the resilient distributed NE seeking algorithm is proposed to ensure that the actions in this algorithm converge to the NE even under DoS attacks. Then, the improved actions with smooth characteristics are designed by introducing novel two-stage cascade filters. By using newly designed actions and their derivatives, a resilient adaptive controller is proposed to ensure that the output of MASs converges to the NE. Finally, simulation results are provided to verify the effectiveness of the proposed strategy.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657090","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

Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-18 DOI: 10.1109/TCYB.2025.3545144

Zhihong Zhao, Tong Wang, Jinyong Yu, Michael V Basin

引用次数: 0