Read by QxMD icon Read

IEEE Transactions on Cybernetics

Pham Duy Hung, Tran Quang Vinh, Trung Dung Ngo
In this paper, we address a novel hierarchical distributed control (HDC) strategy for networked multirobot systems (MRSs). This strategy is developed on a geometric approach without requiring estimation of algebraic connectivity. It is originally based upon behavioral control, but upgraded by distributed node control with a mobility constraint for global network integrity preservation and distributed connectivity control with a local connectivity minimization strategy for network coverage expansion. Thanks to properties of HDC, a networked MRS is capable of achieving high performance with cooperative tasks...
May 16, 2019: IEEE Transactions on Cybernetics
Zengyun Wang, Jinde Cao, Zuowei Cai, Leszek Rutkowski
This paper studies the fixed-time anti-synchronization (FTAS) of discontinuous reaction-diffusion neural networks (DRDNNs) with both time-varying coefficients and time delay. First, differential inclusion theory is used to deal with the influence caused by discontinuous activations. In addition, a new fixed-time convergence theorem is used to handle the time-varying coefficients. Second, a novel state-feedback control algorithm and integral state-feedback control algorithm are proposed to realize FTAS of DRDNNs...
May 16, 2019: IEEE Transactions on Cybernetics
Marcelino Lazaro, Anibal R Figueiras-Vidal
A new method for example-dependent cost (EDC) classification is proposed. The method constitutes an extension of a recently introduced training algorithm for neural networks. The surrogate cost function is an estimate of the Bayesian risk, where the estimates of the conditional probabilities for each class are defined in terms of a 1-D Parzen window estimator of the output of (discriminative) neural networks. This probability density is modeled with the objective of allowing an easy minimization of a sampled version of the Bayes risk...
May 13, 2019: IEEE Transactions on Cybernetics
Chao-Yang Chen, Weihua Gui, Lianghong Wu, Zhaohua Liu, Huaicheng Yan
In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs are considered as continuous-time linear multi-input multioutput (MIMO) systems with random reference noises. The controlled plants include unstable poles and nonminimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder...
May 13, 2019: IEEE Transactions on Cybernetics
Chaoqun Yang, Zhiguo Shi, Heng Zhang, Junfeng Wu, Xiufang Shi
To invade a cyber-physical system (CPS) successfully, hackers are prone to simultaneously launching multiple cyber attacks on different sensors in a CPS. However, little attention has been paid to the problem of detecting multiple cyber attacks up to now. Therefore, in this paper, we deal with the problem on how to efficiently detect multiple cyber attacks aiming at different sensors in CPSs. To achieve the goal of simultaneously detecting both the number of attacks and the attacked sensors, we formulate this problem via a random finite set (RFS) theory, and then apply an iterative RFS-based Bayesian filter and its approximation to solve the problem...
May 13, 2019: IEEE Transactions on Cybernetics
Le Yang, Shiji Song, Shuang Li, Yiming Chen, C L Philip Chen
In this paper, we propose a novel discriminative dimension reduction (DR) method, maximin separation probability analysis (MSPA), which maximizes the minimum separation probability of all classes in the reduced low-dimensional subspace. Separation probability is a novel class separability measure, which gives a lower bound of the generalization accuracy for a learned linear classifier in a binary classification problem. The proposed MSPA duly considers the separation of all class pairs in multiclass linear discriminant analysis (LDA) and thus improves the subsequent classification performance...
May 13, 2019: IEEE Transactions on Cybernetics
Jin-Liang Wang, Dong-Yang Wang, Huai-Ning Wu, Tingwen Huang
In this paper, the output synchronization problem for complex dynamical networks (CDNs) with multiple output or output derivative couplings is discussed in detail. Under the help of Lyapunov functional and inequality techniques, an output synchronization criterion is presented for CDNs with multiple output couplings (CDNMOCs). To ensure the output synchronization of CDNMOCs, an adaptive control scheme is also devised. Similarly, we also take into account the adaptive output synchronization and output synchronization of CDNs with multiple output derivative couplings...
May 13, 2019: IEEE Transactions on Cybernetics
Qirui Zhang, Kun Liu, Yuanqing Xia, Aoyun Ma
This paper studies the problem of designing the optimal deception attack to maximize a utility function with the Kullback-Leibler divergence adopted as a detection constraint. The utility function reflects the goal of pulling the state away from the origin, increasing the cost of the controller and decreasing the cost of the attacker. To analyze the stealthiness of the attack, the attack signal is decomposed into two parts, one of which is strict stealthy. The necessary and sufficient condition is derived for the case that the strict stealthy attack cannot lead to an unbounded benefit...
May 8, 2019: IEEE Transactions on Cybernetics
Huanqing Wang, Peter Xiaoping Liu, Xudong Zhao, Xiaoping Liu
This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness...
May 8, 2019: IEEE Transactions on Cybernetics
Chun Liu, Bin Jiang, Ron J Patton, Ke Zhang
This paper proposes a novel decentralized output sliding-mode fault-tolerant control (FTC) design for heterogeneous multiagent systems (MASs) with matched disturbances, unmatched nonlinear interactions, and actuator faults. The respective iteration and iteration-free algorithms in the sliding-mode FTC scheme are designed with adaptive upper bounding laws to automatically compensate the matched and unmatched components. Then, a continuous fault-tolerant protocol in the observer-based integral sliding-mode design is developed to guarantee the asymptotic stability of MASs and the ultimate boundedness of the estimation errors...
May 6, 2019: IEEE Transactions on Cybernetics
Yin Sheng, Frank L Lewis, Zhigang Zeng, Tingwen Huang
This paper focuses on Lagrange exponential stability and finite-time stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks with discrete and distributed time-varying delays (DFMNNs). By resorting to theories of differential inclusions and the comparison strategy, an algebraic condition is developed to confirm Lagrange exponential stability of the underlying DFMNNs in Filippov's sense, and the exponentially attractive set is estimated. When external input is not considered, global exponential stability of DFMNNs is derived directly, which includes some existing ones as special cases...
May 3, 2019: IEEE Transactions on Cybernetics
Guanglei Zhao, Changchun Hua, Xinping Guan
This paper investigates the leader-following consensus problem for multiagent systems (MASs) with communication delays. A novel hybrid event-triggered control scheme is developed and a hybrid system approach is proposed to design the event-triggering condition. Meanwhile, by means of temporal regularization, a strictly positive lower bound on the interevent times can be guaranteed, that is, Zeno-freeness can be guaranteed. The MASs are first described as a closed-loop system with both flow dynamics and jump dynamics, where the jump dynamics is induced from the triggering events and communication delays...
May 3, 2019: IEEE Transactions on Cybernetics
Yang Zhang, Menglin Cui, Linlin Shen, Zhigang Zeng
Existing deep neural networks (DNNs) are computationally expensive and memory intensive, which hinder their further deployment in novel nanoscale devices and applications with lower memory resources or strict latency requirements. In this paper, a novel approach to accelerate on-chip learning systems using memristive quantized neural networks (M-QNNs) is presented. A real problem of multilevel memristive synaptic weights due to device-to-device (D2D) and cycle-to-cycle (C2C) variations is considered. Different levels of Gaussian noise are added to the memristive model during each adjustment...
May 3, 2019: IEEE Transactions on Cybernetics
Yang Hu, Guihua Wen, Huiqiang Liao, Changjun Wang, Dan Dai, Zhiwen Yu
The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive, and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems...
May 3, 2019: IEEE Transactions on Cybernetics
Yu-Feng Yu, Guoxia Xu, Min Jiang, Hu Zhu, Dao-Qing Dai, Hong Yan
Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously...
May 2, 2019: IEEE Transactions on Cybernetics
Bin Cheng, Zizhen Wu, Zhongkui Li
This paper considers the formation control problem for general linear networked agents constrained with event-triggered communications. We propose four kinds of edge-based event-triggered protocols, each of which can be used to achieve given formation structures and eliminate the unexpected Zeno behavior. Since the whole protocols are designed according to sampled information at event instants rather than real-time information, these protocols efficiently avoid continuous communications, reduce the bandwidth need of communication, and decrease the energy consuming...
May 1, 2019: IEEE Transactions on Cybernetics
Jianhua Xu, Zhi-Hong Mao
Multilabel feature extraction (FE) is an effective preprocessing step to cope with some possible irrelevant, redundant, and noisy features, to reduce computational costs and even improve classification performance. Original normalized cross-covariance operator represents a kernel-based nonlinear dependence measure between features and labels, whose empirical estimator is formulated as a trace operation including two inverse matrices of feature and label kernels with a regularization constant. Due to such a complicated expression, it is impossible to derive an eigenvalue problem for linear FE directly...
April 30, 2019: IEEE Transactions on Cybernetics
Jiejie Chen, Boshan Chen, Zhigang Zeng, Ping Jiang
This paper deals with global exponential synchronization of multiple neural networks (NNs) with time delay via a very broad class of event-triggered coupling, in which coupling matrix can be non-Laplacian. Some simple and convenient sufficient conditions are derived to guarantee global exponential synchronization of the coupling NNs under an event-triggered strategy. In particular, the effect of the common subsystem can be positive or negative on the synchronization scheme. Three examples are presented to test the results in theory analysis...
April 29, 2019: IEEE Transactions on Cybernetics
Kun Cao, Xiuxian Li, Lihua Xie
This paper studies the dynamic formation control problem for cooperative agents with discrete-time dynamics over directed graphs. Unlike using absolute coordinate, relative coordinate, interagent distance, or interagent bearing to specify the target formation and coordinate agents to achieve the formation, we study a coordination problem where the desired formation varies with time and only its geometric shape is predefined. Matrix-valued Laplacian approach has been adopted to address this problem in the continuous-time setting...
April 29, 2019: IEEE Transactions on Cybernetics
Feiping Nie, Zheng Wang, Rong Wang, Xuelong Li
Due to the multimodality of non-Gaussian data, traditional globality-preserved dimensionality reduction (DR) methods, such as linear discriminant analysis (LDA) and principal component analysis (PCA) are difficult to deal with. In this paper, we present a novel local DR framework via auto-optimized graph embedding to extract the intrinsic submanifold structure of multimodal data. Specifically, the proposed model seeks to learn an embedding space which can preserve the local neighborhood structure by constructing a k-nearest neighbors (kNNs) graph on data points...
April 26, 2019: IEEE Transactions on Cybernetics
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"