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Dynamic Surface Intelligent Robust Control of Nonlinear Systems With Fixed-Time Sliding-Mode Observer IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Hong-Gui Han, Cheng-Cheng Feng, Hao-Yuan Sun, Jun-Fei Qiao
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Minimum-Cost State-Flipped Control for Reachability of Boolean Control Networks Using Reinforcement Learning IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Jingjie Ni, Yang Tang, Fangfei Li
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Secure Decentralized Event-Triggered Load Frequency Control Design for Multiarea Power Systems Under Multiple DoS Attacks IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Kun Xu, Yugang Niu, James Lam
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Explicit Evolutionary Framework With Multitasking Feature Fusion for Optimizing Operational Parameters in Aluminum Electrolysis Process IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Lizhong Yao, Xin Zong, Ling Wang, Rui Li, Jun Yi
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Indefinite Robust Linear Quadratic Optimal Regulator for Discrete-Time Uncertain Singular Markov Jump Systems IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Yichun Li, Wei Xing Zheng, Zheng-Guang Wu, Yang Tang, Shuping Ma
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Fully Event-Triggered Practical Leader–Following Consensus of Multiple Euler–Lagrange Systems Over Switching Networks IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Rui Zhang, Jie Huang
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Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems With Output Constraints and DoS Attacks IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-17 Yongming Li, Ge Lu, Kewen Li
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Event-Triggered Adaptive Preassigned Finite-Time Consensus Control for Multiagent Systems With Nonlinear Faults IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-12 Yasaman Salmanpour, Mohammad Mehdi Arefi, Jinde Cao
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Almost Sure Stability and Stabilization of Composite Switched Systems With Persistent Dwell Time Constraint IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-12 Wenshuai Gao, Yang Song
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Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-12 Ying Hou, Xinyu Guo, Honggui Han, Jingjing Wang, Yongping Du
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Fuzzy Clustering Guided by Spectral Rotation and Scaling IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-11 Jie Zhou, Ge Yuan, Can Gao, Xizhao Wang, Jianhua Dai, Witold Pedrycz
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Co-Clustering by Directly Solving Bipartite Spectral Graph Partitioning IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-10 Jingjing Xue, Feiping Nie, Chaodie Liu, Rong Wang, Xuelong Li
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Enhancing Collaboration in Heterogeneous Multiagent Systems Through Communication Complementary Graph IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-10 Kexing Peng, Tinghuai Ma, Li Jia, Huan Rong
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Control of Uncertain High-Order Fully Actuated Strict-Feedback Systems: A Backstepping Approach With High-Gain Observer-Based Derivative Approximation IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-10 Weizhen Liu, Guangren Duan, Mingzhe Hou, Mehdi Golestani, He Kong
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Secure Control for Markov Jump Cyber–Physical Systems Subject to Malicious Attacks: A Resilient Hybrid Learning Scheme IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-06 Hao Shen, Yun Wang, Jiacheng Wu, Ju H. Park, Jing Wang
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Noncooperative Game Strategy Design Over Fading Measurement Channel Under Wiener Type Disturbance: Handling Continuous-Time Time-Varying System IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-05 Yuan Yuan, Lei Cheng, Min Shi
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Observer-Based Optimal Backstepping Security Control for Nonlinear Systems Using Reinforcement Learning Strategy IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-04 Qinglai Wei, Wendi Chen, Xiangmin Tan, Jun Xiao, Qi Dong
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Robust Subcluster Search and Mergence Clustering IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-04 Bocheng Wang, Mulin Chen, Xuelong Li
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A Graph-Based Time–Frequency Two-Stream Network for Multistep Prediction of Key Performance Indicators in Industrial Processes IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-04 Feng Yan, Xinmin Zhang, Chunjie Yang
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Cooperative Localization for Asynchronous AUVs Using Time Difference of Communication in Underwater Anchor-Free Environments IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-04 Liangyu Jiang, Yichen Li, Wenbin Yu, Xinping Guan
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Distributed Asynchronous Constrained Output Formation Optimal Tracking for Multiagent Systems With Intermittent Communications IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-04 Lingfei Su, Yongzhao Hua, Xiwang Dong, Jinhu Lü, Zhang Ren
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Bearing-Constrained Leader-Follower Formation of Single-Integrators With Disturbance Rejection: Adaptive Variable-Structure Approaches IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-09-02 Thanh Truong Nguyen, Dung Van Vu, Tuynh Van Pham, Minh Hoang Trinh
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Unscented-Kalman-Filter-Based Remote State Estimation for Complex Networks With Quantized Measurements and Amplify-and-Forward Relays IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-30 Tong-Jian Liu, Zidong Wang, Yang Liu, Rui Wang
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Robust 3-D Path Following Control Framework for Magnetic Helical Millirobots Subject to Fluid Flow and Input Saturation IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-29 Zhaoyang Qi, Mingxue Cai, Bo Hao, Yanfei Cao, Lin Su, Xurui Liu, Kai Fung Chan, Chenguang Yang, Li Zhang
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Resilient Cruise Control of Heterogeneous Platoons Against Byzantine Attacks: Theory and Experiment IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-29 Xin Gong, Yong Chen, Fuda Zou, Wangkui Liu, Jun Shen, Zhan Shu
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Dual Channels Event-Triggered Asymptotic Consensus Control for Fractional-Order Nonlinear Multiagent Systems IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-29 Yang Liu, Xiangpeng Xie, Reinaldo Martinez Palhares, Jiayue Sun
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Event-Triggered Distributed Hypothesis Testing for Multiagent Networks Based on Observations Cumulation IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-28 Chong-Xiao Shi, Guang-Hong Yang
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ADP-Based Decentralized Load Frequency Control Schemes to Multiarea Asynchronous Markov Jumping Power Systems With Experience Replay IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-26 Hai Wang, Jun Cheng, Shengda Tang, Iyad Katib, Xuan Qiu
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Adaptive Security Control Using Output Only for Quantized Nonlinear Systems Under Irregularly Intermittent DoS Attacks IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-26 Zhen Gao, Yongduan Song, Marios M. Polycarpou
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Optimized Backstepping-Based Containment Control for Multiagent Systems With Deferred Constraints Using a Universal Nonlinear Transformation IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-23 Xiaohui Yue, Huaguang Zhang, Jiayue Sun, Tianbiao Wang, Lu Liu
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Solving Expensive Optimization Problems in Dynamic Environments With Meta-Learning IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-23 Huan Zhang, Jinliang Ding, Liang Feng, Kay Chen Tan, Ke Li
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Observer-Based Asynchronous Boundary Stabilization for Stochastic Markovian Reaction–Diffusion Neural Networks IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-23 Xin-Xin Han, Kai-Ning Wu, Xin Yuan
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Consensus for Heterogeneous Multiagent Systems: Output Rate-Coded Secure Control IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-22 Ruihang Ji, Shuzhi Sam Ge
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Differentiated Anchor Quantity Assisted Incomplete Multiview Clustering Without Number-Tuning IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-22 Shengju Yu, Pei Zhang, Siwei Wang, Zhibin Dong, Hengfu Yang, En Zhu, Xinwang Liu
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Sparse Bayesian Learning for Switching Network Identification. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-20 Yaozhong Zheng,Hai-Tao Zhang,Zuogong Yue,Jun Wang
Learning dynamical networks based on time series of nodal states is of significant interest in systems science, computer science, and control engineering. Despite recent progress in network identification, most research focuses on static structures rather than switching ones. Therefore, this article develops a method for identifying the structures of switching networks by exploring and leveraging both
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An Accelerated Adaptive Gain Design in Stochastic Learning Control IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Xiang Cheng, Hao Jiang, Dong Shen, Xinghuo Yu
This study investigates the trajectory tracking problem for stochastic systems and proposes a novel adaptive gain design to enhance the transient convergence performance of the learning control scheme. Differing from the existing results that mainly focused on gain’s transition from constant to decreasing ones to suppress noise influence, this study leverages the adaptive mechanisms based on noisy
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An Aperiodic-Sampling-Dependent Event-Triggered Control Strategy for Interval Type-2 Fuzzy Systems: New Communication Scheme and Discontinuous Functional IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Zhaoliang Sheng, Shengyuan Xu
This article studies the event-triggered control problem of interval type-2 (IT2) fuzzy systems. It proposes a new aperiodic-sampling-dependent event-triggered communication scheme, and introduces an improved sampling-dependent discontinuous functional. First, taking into account that the system is with aperiodic sampling, the weighting matrices used for judgement in the event-triggered scheme are
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Data-Driven Tube-Based Robust Predictive Control for Constrained Wastewater Treatment Process IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Hong-Gui Han, Yan Wang, Hao-Yuan Sun, Zheng Liu, Jun-Fei Qiao
The wastewater treatment process (WWTP) is characterized by unknown nonlinearity and external disturbances, which complicates the tracking control of dissolved oxygen concentration (DOC) within operational constraints. To address this issue, a data-driven tube-based robust predictive control (DTRPC) strategy is proposed to achieve stable tracking control of DOC and satisfy the system constraints. First
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Adaptive Distributed Control of Nonlinear Multiagent Systems With Event-Triggered for Communication Faults and Dead-Zone Inputs IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Jiayue Sun, Zhiming Xu, Huaguang Zhang, Tianyou Chai, Shiyang Wang
This article studies the containment control problem of nonlinear multiagent systems (MASs) subjected to communication link faults and dead-zone inputs. In case of an unknown fault in the communication link, there is no constant Laplacian matrix anymore and each follower agent cannot be informed of the global information simultaneously. To deal with this problem, an adaptive compensating estimator
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Event-Based Asynchronous $H_{\infty}$ Control for Nonhomogeneous Markov Jump Systems With Imperfect Transition Probabilities IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Yifang Zhang, Zheng-Guang Wu
The event-based H∞H_{\infty} control problem is investigated for a class of nonhomogeneous Markov jump systems (MJSs) with partially unknown transition probabilities (TPs). The MJS is characterized by a piecewise nonhomogeneous Markovian chain, where the switching of the system TP matrix is governed by a higher-level chain. A hidden Markov model (HMM) is employed to observe the system mode, which cannot
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Label-Specific Time–Frequency Energy-Based Neural Network for Instrument Recognition IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Jian Zhang, Tong Wei, Min-Ling Zhang
Predominant instrument recognition plays a vital role in music information retrieval. This task involves identifying and categorizing the dominant instruments present in a piece of music based on their distinctive time_frequency characteristics and harmonic distribution. Existing predominant instrument recognition approaches mainly focus on learning implicit mappings (such as deep neural networks)
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Fault Reconstruction Algorithm for Fractional-Order Nonlinear Switching Systems Based on Optimal Fault-Tolerant Control IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-08-19 Yuqing Yan, Huaguang Zhang, Wenyue Zhao, Mei Li
In this article, a novel fault reconstruction algorithms for fractional-order nonlinear switching systems (FONSSs) with actuator and sensor faults are investigated. First, fractional-order nonlinear system (FONS) with faults, is transformed into two fast and slow subsystems using global differential homogeneous transformation, one of which is unaffected by the fault and the state is partially observable;
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State Responses of Several Classes of Linear Systems Based on Fundamental Matrices IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-05-07 Ai-Guo Wu, Yu-Tian Xu, Jie Mei
State responses for several classes of linear systems are investigated in this article. The involved systems include state-delayed linear systems, and high-order linear systems. At first, the single-fundamental-matrix-based approach is extended to these systems, and their state responses are expressed by their fundamental matrices (FMs). In addition, the multiple-FMs-based approach is presented for
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Integral BLF-Based Adaptive Dynamic Event-Triggered Boundary Control for a Flexible Riser System IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-05-07 Xin-Yu Zhang, Xiangpeng Xie, Ju H. Park, Yajuan Liu, Jiayue Sun
For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome
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MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-05-07 Jing Jin, Ruitian Xu, Ian Daly, Xueqing Zhao, Xingyu Wang, Andrzej Cichocki
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities
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Actor-Critic With Synthesis Loss for Solving Approximation Biases IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-05-03 Bo-Wen Guo, Fei Chao, Xiang Chang, Changjing Shang, Qiang Shen
Approximation biases of value functions are considered a key problem in reinforcement learning (RL). In particular, existing RL algorithms are hindered by overestimation and underestimation biases, i.e., value mismatching between RL’s actual returns and action-value approximations limits the performance of RL algorithms. In this article, we first develop a new synthesis loss function for RL’s action-value
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Data-Driven Dynamic Internal Model Control IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-30 Ronghu Chi, Huimin Zhang, Huaying Li, Biao Huang, Zhongsheng Hou
A data-driven dynamic internal model control (D3IMC) scheme is proposed for unknown nonlinear nonaffine systems bypassing modeling steps. Different from the traditional internal model constructed by either a first-principle or an identified model, a dynamic internal model (DIM) is developed in this work using I/O data where a compact form dynamic linearization approach is introduced for addressing
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Optimized Backstepping Cooperative Control for Output-Constrained Stochastic Nonlinear Network Systems via a Multibridge-Hole Function IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-19 Xiyue Guo, Huaguang Zhang, Xiaohui Yue, Tianbiao Wang
In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function
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An Efficient Robotic Pushing and Grasping Method in Cluttered Scene IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-17 Sheng Yu, Di-Hua Zhai, Yuanqing Xia, Yuyin Guan
Pushing and grasping (PG) are crucial skills for intelligent robots. These skills enable robots to perform complex grasping tasks in various scenarios. These PG methods can be categorized into single-stage and multistage approaches. Single-stage methods are faster but less accurate, while multistage methods offer high accuracy at the expense of time efficiency. To address this issue, a novel end-to-end
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Logic-Based Fixed-Time Control for Uncertain Nonlinear Systems With Unknown Control Directions IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-17 Zhonghua Sun, Changchun Hua
The fixed-time control problem is investigated for a class of uncertain nonlinear systems subjected to multiple unknown control directions. The control coefficients of nonlinear systems under consideration are time varying and their signs are not required to be identical. To tackle this challenge, a switching mechanism along with a novel dynamic boundary function is proposed. Utilizing the devised
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A Novel Set-Based Discrete Particle Swarm Optimization for Wastewater Treatment Process Effluent Scheduling IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-15 Hong-Gui Han, Zi-Ang Xu, Jing-Jing Wang
With the escalating severity of environmental pollution caused by effluent, the wastewater treatment process (WWTP) has gained significant attention. The wastewater treatment efficiency and effluent quality are significantly impacted by effluent scheduling that adjusts the hydraulic retention time. However, the sequential batch and continuous nature of the effluent pose challenges, resulting in complex
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Two-Layer Reinforcement Learning for Output Consensus of Multiagent Systems Under Switching Topology IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-10 Zhanshan Wang, Yingying Liu, Huaguang Zhang
In this article, the data-based output consensus of discrete-time multiagent systems under switching topology (ST) is studied via reinforcement learning. Due to the existence of ST, the kernel matrix of value function is switching-varying, which cannot be applied to existing algorithms. To overcome the inapplicability of varying kernel matrix, a two-layer reinforcement learning algorithm is proposed
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Multiple Observer Adaptive Fusion for Uncertainty Estimation and Its Application to Wheel Velocity Systems IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-10 Zhi Zheng, Tao Jiang, Xiaojie Su, Jiangshuai Huang, Xiaoyu Ma
Uncertainty estimation in real-world scenarios is challenged by complexities arising from peaking phenomena and measurement noises. This article introduces a novel scheme for practical uncertainty estimation to mitigate peaking dynamics and enhance overall dynamic behavior. A fusion estimation framework for lumped uncertainties using multiple extended state observers (ESOs) is constructed, and the
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Exploring Robust Features for Improving Adversarial Robustness IEEE Trans. Cybern. (IF 9.4) Pub Date : 2024-04-09 Hong Wang, Yuefan Deng, Shinjae Yoo, Yuewei Lin
While deep neural networks (DNNs) have revolutionized many fields, their fragility to carefully designed adversarial attacks impedes the usage of DNNs in safety-critical applications. In this article, we strive to explore the robust features that are not affected by the adversarial perturbations, that is, invariant to the clean image and its adversarial examples (AEs), to improve the model’s adversarial