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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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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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Identification of Network Topology Variations Based on Spectral Entropy. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Housheng Su,Dan Chen,Gui-Jun Pan,Zhigang Zeng
Based on the fact that the traditional probability distribution entropy describing a local feature of the system cannot effectively capture the global topology variations of the network, some indicators constructed by the network adjacency matrix and Laplacian matrix come into being. Specifically, these measures are based on the eigenvalues of the scaled Laplace matrix, the eigenvalues of the network
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Cold-Start Active Sampling via ɣ-Tube. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Xiaofeng Cao,Ivor W Tsang,Jianliang Xu
Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its performance in a cold-start hypothesis. In this article
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Distributed Bipartite Adaptive Event-Triggered Fault-Tolerant Consensus Tracking for Linear Multiagent Systems Under Actuator Faults. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Yuliang Cai,Huaguang Zhang,Weihua Li,Yunfei Mu,Qiang He
This article considers the distributed bipartite adaptive event-triggered fault-tolerant consensus tracking issue for linear multiagent systems in the presence of actuator faults based on the output feedback control protocol. Both time-varying additive and multiplicative actuator faults are taken into account in the meantime. And the upper/lower bounds of actuator faults are not required to be known
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A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Ya-Hui Jia,Yi Mei,Mengjie Zhang
The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together
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Identification of Fuzzy Rule-Based Models With Collaborative Fuzzy Clustering. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Xingchen Hu,Yinghua Shen,Witold Pedrycz,Xianmin Wang,Adam Gacek,Bingsheng Liu
Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of
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Saliency-Based Multilabel Linear Discriminant Analysis. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Lei Xu,Jenni Raitoharju,Alexandros Iosifidis,Moncef Gabbouj
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new variant of LDA to be used in multilabel classification
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Resetting Weight Vectors in MOEA/D for Multiobjective Optimization Problems With Discontinuous Pareto Front. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Chunjiang Zhang,Liang Gao,Xinyu Li,Weiming Shen,Jiajun Zhou,Kay Chen Tan
When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered
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Nonsynchronous Model Reduction for Uncertain 2-D Markov Jump Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Ying Shen,Zheng-Guang Wu,Deyuan Meng
Mode information is of great significance when investigating the Markov jump systems (MJSs). However, it is common in practical scenarios that the mode information is not completely accessible, which probably induces nonsynchronization problems. Taking this into consideration, in this article, we study nonsynchronous H∞ model order reduction for 2-D MJSs with model uncertainty. The considered 2-D system
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Adaptive Fuzzy Decentralized Dynamic Surface Control for Switched Large-Scale Nonlinear Systems With Full-State Constraints. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Jing Zhang,Shi Li,Choon Ki Ahn,Zhengrong Xiang
In this study, an adaptive fuzzy decentralized dynamic surface control (DSC) problem is investigated for switched large-scale nonlinear systems with deferred asymmetric and time-varying full-state constraints. Due to the existence of additional general nonlinearities, complicated output interconnections, and full-state constraints, it is difficult to address the above control problem using existing
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H∞ Codesign for Uncertain Nonlinear Control Systems Based on Policy Iteration Method. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Quan-Yong Fan,Dongsheng Wang,Bin Xu
In this article, the problem of H∞ codesign for nonlinear control systems with unmatched uncertainties and adjustable parameters is investigated. The main purpose is to solve the adjustable parameters and H∞ controller simultaneously so that better robust control performance can be achieved. By introducing a bounded function and defining a special cost function, the problem of solving the Hamilton-Jacobi-Isaacs
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Impulsive Communication With Full and Partial Information for Adaptive Tracking Consensus of Uncertain Second-Order Multiagent Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Yiyan Han,Zhigang Zeng
The uncertainty of dynamics is often unavoidable in practical applications especially for networked control systems while energy cost reduction is a perpetual issue for engineering. With this motivation, this article considers the adaptive tracking consensus problem of uncertain second-order multiagent systems via impulsive communication. The tracking consensus is achieved by designing proper adaptive
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Adaptive Neural Network Fixed-Time Control Design for Bilateral Teleoperation With Time Delay. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Shuang Zhang,Shuo Yuan,Xinbo Yu,Linghuan Kong,Qing Li,Guang Li
In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then
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Inverse Reinforcement Learning in Tracking Control Based on Inverse Optimal Control. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Wenqian Xue,Patrik Kolaric,Jialu Fan,Bosen Lian,Tianyou Chai,Frank L Lewis
This article provides a novel inverse reinforcement learning (RL) algorithm that learns an unknown performance objective function for tracking control. The algorithm combines three steps: 1) an optimal control update; 2) a gradient descent correction step; and 3) an inverse optimal control (IOC) update. The new algorithm clarifies the relation between inverse RL and IOC. It is shown that the reward
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Time-Varying Group Formation-Containment Tracking Control for General Linear Multiagent Systems With Unknown Inputs. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Yizhou Lu,Xiwang Dong,Qingdong Li,Jinhu Lu,Zhang Ren
Time-varying group formation-containment tracking problems for general linear multiagent systems with unknown control input are investigated. Agents are classified into tracking leaders, formation leaders, and followers and assigned in groups. Tracking leaders with unknown control inputs provide unpredictable trajectories as macroscopic moving references. Formation leaders accomplish desired subformations
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Asymmetric Input-Output Constraint Control of a Flexible Variable-Length Rotary Crane Arm. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-20 Yu Liu,Yanfang Mei,He Cai,Changran He,Tao Liu,Guoqiang Hu
This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations
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Many-Objective Evolutionary Algorithm With Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job Shop Scheduling With Limited Workers. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Wenfeng Li,Lijun He,Yulian Cao
Because of COVID-19, factories are facing many difficulties, such as shortage of workers and social alienation. How to improve production performance under limited labor resources is an urgent problem for global manufacturing factories. This work studies an energy-efficient job-shop scheduling problem with limited workers. Those workers can have multiskills. A many-objective model with five objectives
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Robust Tensor SVD and Recovery With Rank Estimation. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Qiquan Shi,Yiu-Ming Cheung,Jian Lou
Tensor singular value decomposition (t-SVD) has recently become increasingly popular for tensor recovery under partial and/or corrupted observations. However, the existing t-SVD-based methods neither make use of a rank prior nor provide an accurate rank estimation (RE), which would limit their recovery performance. From the practical perspective, the tensor RE problem is nontrivial and difficult to
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Deep Manifold Embedding for Hyperspectral Image Classification. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Zhiqiang Gong,Weidong Hu,Xiaoyong Du,Ping Zhong,Panhe Hu
Deep learning methods have played a more important role in hyperspectral image classification. However, general deep learning methods mainly take advantage of the samplewise information to formulate the training loss while ignoring the intrinsic data structure of each class. Due to the high spectral dimension and great redundancy between different spectral channels in the hyperspectral image, these
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Energy-Saving Optimization and Control of Autonomous Electric Vehicles With Considering Multiconstraints. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Ying Zhang,Zhaoyang Ai,Jinchao Chen,Tao You,Chenglie Du,Lei Deng
The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the
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Energy-Saving Robust Saturated Control for Active Suspension Systems via Employing Beneficial Nonlinearity and Disturbance. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Menghua Zhang,Xingjian Jing
This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing
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Adaptive Optimized Backstepping Control-Based RL Algorithm for Stochastic Nonlinear Systems With State Constraints and Its Application. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Yongming Li,Yanli Fan,Kewen Li,Wei Liu,Shaocheng Tong
This article investigates the adaptive neural-network (NN) tracking optimal control problem for stochastic nonlinear systems, which contain state constraints and uncertain dynamics. First, to avoid the violation of state constraints in achieving optimal control, the novel barrier optimal performance index functions for subsystems are developed. Second, under the framework of the identifier-actor-critic
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Fuzzy SMC for Quantized Nonlinear Stochastic Switching Systems With Semi-Markovian Process and Application. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Wenhai Qi,Xu Yang,Ju H Park,Jinde Cao,Jun Cheng
This article is concerned with the issue of quantized sliding-mode control (SMC) design methodology for nonlinear stochastic switching systems subject to semi-Markovian switching parameters, T-S fuzzy strategy, uncertainty, signal quantization, and nonlinearity. Compared with the previous literature, the quantized control input is first considered in studying T-S fuzzy stochastic switching systems
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Dissipativity-Based Fault-Tolerant Control for Stochastic Switched Systems With Time-Varying Delay and Uncertainties. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Jiayue Sun,Huaguang Zhang,Yingchun Wang,Zhan Shi
This article investigates the fault-tolerant control problem for stochastic switched interval type-2 (IT2) fuzzy time-delayed uncertain systems based on unknown input observer synthesis, which can avoid uneasy measurement on the time derivative of output, and estimate unavailable or partially measurable states, including sensor and actuator faults accurately. First, a desired fuzzy observer is designed
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Kullback-Leibler Divergence-Based Optimal Stealthy Sensor Attack Against Networked Linear Quadratic Gaussian Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Xiu-Xiu Ren,Guang-Hong Yang
This article concentrates on designing optimal stealthy attack strategies for cyber-physical systems (CPSs) modeled by the linear quadratic Gaussian (LQG) dynamics, where the attacker aims to increase the quadratic cost maximally and keeping a certain level of stealthiness by simultaneously intercepting and modifying the transmitted measurements. In our work, a novel attack model is developed, based
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Accelerated Log-Regularized Convolutional Transform Learning and its Convergence Guarantee. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Zhenni Li,Haoli Zhao,Yongcheng Guo,Zuyuan Yang,Shengli Xie
Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the success of the convolutional neural network. There have been growing interests in developing efficient CTL algorithms. However, developing a convergent and
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Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Qi Wang,Ran Liu,Mulin Chen,Xuelong Li
Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is difficult to produce a high-quality one, especially when data contain noises and outliers. To solve this problem, we propose a robust rank constrained sparse learning
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Optimal Bounded Ellipsoid Identification With Deterministic and Bounded Learning Gains: Design and Application to Euler-Lagrange Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Kai Guo,Dong-Dong Zheng,Jianyong Li
This article proposes an effective optimal bounded ellipsoid (OBE) identification algorithm for neural networks to reconstruct the dynamics of the uncertain Euler-Lagrange systems. To address the problem of unbounded growth or vanishing of the learning gain matrix in classical OBE algorithms, we propose a modified OBE algorithm to ensure that the learning gain matrix has deterministic upper and lower
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Continuous Terminal Sliding-Mode Control for FJR Subject to Matched/Mismatched Disturbances. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Huiming Wang,Qiyao Zhang,Zhenxing Sun,Xianlun Tang,I-Ming Chen
A robust finite-time control (FTC) framework using continuous terminal sliding-mode control (SMC) and high-order sliding-mode observer (HOSMO) is discussed to realize the trajectory tracking of flexible-joint robots in this article. Control performances of the robots always suffer from unknown matched and mismatched time-varying disturbances. Traditional SMC exists with a chattering phenomenon and
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Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral Clustering. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-19 Yuting Wan,Ailong Ma,Liangpei Zhang,Yanfei Zhong
Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote
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Input-to-State Stabilization of Stochastic Markovian Jump Systems Under Communication Constraints: Genetic Algorithm-Based Performance Optimization. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-06 Bei Chen,Yugang Niu,Hongjian Liu
This work investigates the stabilization problem of uncertain stochastic Markovian jump systems (MJSs) under communication constraints. To reduce the bandwidth usage, a discrete-time Markovian chain is employed to implement the stochastic communication protocol (SCP) scheduling of the sensor nodes, by which only one sensor node is chosen to access the network at each transmission instant. Moreover
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Model-Free Containment Control of Underactuated Surface Vessels Under Switching Topologies Based on Guiding Vector Fields and Data-Driven Neural Predictors. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-06 Nan Gu,Dan Wang,Zhouhua Peng,Tieshan Li,Shaocheng Tong
This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according
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Handling Constrained Multiobjective Optimization Problems via Bidirectional Coevolution. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-04-06 Zhi-Zhong Liu,Bing-Chuan Wang,Ke Tang
Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective functions and various constraints. Due to the presence of constraints, CMOPs' Pareto-optimal solutions are very likely lying on constraint boundaries. The experience from the constrained single-objective optimization has shown that to quickly obtain such an optimal solution, the search should surround the boundary
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Adaptive Finite-Time Tracking Control of Nonholonomic Multirobot Formation Systems With Limited Field-of-View Sensors. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-23 Shi-Lu Dai,Ke Lu,Jun Fu
This article studies the vision-based tracking control problem for a nonholonomic multirobot formation system with uncertain dynamic models and visibility constraints. A fixed onboard vision sensor that provides the relative distance and bearing angle is subject to limited range and angle of view due to limited sensing capability. The constraint resulting from collision avoidance is also taken into
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Event-Triggered Resilient L∞ Control for Markov Jump Systems Subject to Denial-of-Service Jamming Attacks. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-23 Pengyu Zeng,Feiqi Deng,Xiaohua Liu,Xiaobin Gao
In this article, the event-triggered resilient L∞ control problem is concerned for the Markov jump systems in the presence of denial-of-service (DoS) jamming attacks. First, a fixed lower bound-based event-triggering scheme (ETS) is presented in order to avoid the Zeno problem caused by exogenous disturbance. Second, when DoS jamming attacks are involved, the transmitted data are blocked and the old
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Adaptive Fuzzy Finite-Time Control for Nonstrict-Feedback Nonlinear Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-23 Yongchao Liu,Qidan Zhu
This article presents an adaptive fuzzy finite-time control (AFFTC) method for nonstrict-feedback nonlinear systems (NFNSs) with unknown dynamics. With the aid of the backstepping technique, by establishing the smooth switch function (SSF), a novel C¹ AFFTC strategy is recursively constructed, which counteracts the effect of nonstrict-feedback structure and unknown dynamics. Different from the reporting
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Robust Standard Gradient Descent Algorithm for ARX Models Using Aitken Acceleration Technique. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-23 Jing Chen,Min Gan,Quanmin Zhu,Pritesh Narayan,Yanjun Liu
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration method is developed. Considering that the SGD algorithm has slow convergence rates and is sensitive to the step size, a robust and accelerative SGD (RA-SGD) algorithm is derived. This algorithm is based on the Aitken acceleration method, and its convergence rate is improved from linear convergence to at
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Adversarial Incomplete Multiview Subspace Clustering Networks. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-22 Cai Xu,Hongmin Liu,Ziyu Guan,Xunlian Wu,Jiale Tan,Beilei Ling
Multiview clustering aims to leverage information from multiple views to improve the clustering performance. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the problem of incomplete multiview clustering (IMC). Previous approaches to this problem have at least one of the following
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Fully Distributed Adaptive Event-Triggered Control of Networked Systems With Actuator Bias Faults. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-22 Yong Xu,Jian Sun,Zheng-Guang Wu,Gang Wang
In this article, the problem of distributed synchronization of networked systems with actuator bias faults is investigated. To effectively use the limited network bandwidth and avoid the requirement of global information, a novel adaptive event-triggered state feedback controller and a dynamic triggering law are designed jointly by employing a projection operator approach. The proposed synchronization
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Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-22 Zhong Yuan,Hongmei Chen,Tianrui Li,Binbin Sang,Shu Wang
Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data. There are few studies on the outlier detection of mixed attribute data. In this article, we introduce fuzzy rough sets (FRSs) to deal with the problem of outlier detection in mixed attribute data. Since the
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New Admissibility and Admissibilization Criteria for Nonlinear Discrete-Time Singular Systems by Switched Fuzzy Models. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-17 Jian Chen,Jinpeng Yu,Hak-Keung Lam
Admissibility analysis and control synthesis for nonlinear discrete-time singular systems are considered in this article. With regard to the type-1 and interval type-2 fuzzy singular systems, the partition of membership functions and scale transform is imposed, and new switched fuzzy systems, which are equivalent to the original systems, are established. A relaxed stability criterion is derived to
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Differential Graphical Games for Constrained Autonomous Vehicles Based on Viability Theory. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-17 Bowen Peng,Alexandru Stancu,Shuping Dang,Zhengtao Ding
This article proposes an optimal-distributed control protocol for multivehicle systems with an unknown switching communication graph. The optimal-distributed control problem is formulated to differential graphical games, and the Pareto optimum to multiplayer games is sought based on the viability theory and reinforcement learning techniques. The viability theory characterizes the controllability of
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Runtime Safety Monitoring of Neural-Network-Enabled Dynamical Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-17 Weiming Xiang
Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stages of modeling, sensing, and control at both offline and online levels. This article addresses the runtime safety monitoring problem of dynamical systems embedded with neural-network components. A runtime safety state estimator
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Secure Finite-Horizon Consensus Control of Multiagent Systems Against Cyber Attacks. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-17 Xiao-Meng Li,Deyin Yao,Panshuo Li,Wei Meng,Hongyi Li,Renquan Lu
The problem of secure finite-horizon consensus control for discrete time-varying multiagent systems (MASs) with actuator saturation and cyber attacks is addressed in this article. A random attack model is first proposed to account for randomly occurring false data injection attacks and denial-of-service attacks, whose dynamics are governed by the random Markov process. The hybrid secure control scheme
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Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-17 Ye Tian,Yajie Zhang,Yansen Su,Xingyi Zhang,Kay Chen Tan,Yaochu Jin
Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies
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Two-Stage Selective Ensemble of CNN via Deep Tree Training for Medical Image Classification. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Yun Yang,Yuanyuan Hu,Xingyi Zhang,Song Wang
Medical image classification is an important task in computer-aided diagnosis systems. Its performance is critically determined by the descriptiveness and discriminative power of features extracted from images. With rapid development of deep learning, deep convolutional neural networks (CNNs) have been widely used to learn the optimal high-level features from the raw pixels of images for a given classification
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Fuzzy Adaptive Decentralized Control for Nonstrict-Feedback Large-Scale Switched Fractional-Order Nonlinear Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Wenshan Bi,Tong Wang,Xinghu Yu
This article investigates the adaptive fuzzy control algorithm for a class of large-scale switched fractional-order nonlinear nonstrict feedback systems. In this algorithm, we utilize fuzzy-logic systems (FLSs) to approximate the complicated unknown nonlinear functions. Based on the fractional Lyapunov stability rules, a virtual control law is presented. A fuzzy adaptive decentralized control method
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Fully Distributed Event-Triggered Optimal Coordinated Control for Multiple Euler-Lagrangian Systems. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Yi Huang,Ziyang Meng
This article studies a fully distributed optimal coordinated control problem with the global cost function for networked Euler-Lagrange (EL) systems subject to unknown model parameters. In particular, the global cost function is the sum of all the local cost functions assigned to each agent and only available to itself. The objective is to minimize the global cost function in a distributed manner while
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Coordinated Planar Path-Following Control for Multiple Nonholonomic Wheeled Mobile Robots. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Zongyu Zuo,Jiawei Song,Qing-Long Han
This article is concerned with both consensus and coordinated path-following control for multiple nonholonomic wheeled mobile robots. In the design, the path-following control is decoupled into the longitudinal control (speed control) and the lateral control (heading control) for the convenience of implementation. Different from coordinated trajectory tracking control schemes, the proposed control
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Event-Triggered Adaptive Neural Control for Fractional-Order Nonlinear Systems Based on Finite-Time Scheme. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Yuan-Xin Li,Ming Wei,Shaocheng Tong
This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteria are introduced with the aim to ensure that the tracking error enters into a small region around the origin in finite time. Finally, the stability of
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Multimodal Gait Recognition for Neurodegenerative Diseases. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Aite Zhao,Jianbo Li,Junyu Dong,Lin Qi,Qianni Zhang,Ning Li,Xin Wang,Huiyu Zhou
In recent years, single modality-based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognized that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multimodality analysis of the
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Information Fusion Fault Diagnosis Method for Deep-Sea Human Occupied Vehicle Thruster Based on Deep Belief Network. IEEE Trans. Cybern. (IF 9.4) Pub Date : 2021-03-11 Daqi Zhu,Xuelong Cheng,Lei Yang,Yunsai Chen,Simon X Yang
In this article, a novel thruster information fusion fault diagnosis method for the deep-sea human occupied vehicle (HOV) is proposed. A deep belief network (DBN) is introduced into the multisensor information fusion model to identify uncertain and unknown, continuously changing fault patterns of the deep-sea HOV thruster. Inputs for the DBN information fusion fault diagnosis model are the control