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IEEE Transactions on Cybernetics

Yumei Sun, Bing Chen, Chong Lin, Honghong Wang
This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded...
September 18, 2017: IEEE Transactions on Cybernetics
Chen Xu, Xinsong Yang, Jianquan Lu, Jianwen Feng, Fuad E Alsaadi, Tasawar Hayat
This technical correspondence considers finite-time synchronization of dynamical networks by designing aperiodically intermittent pinning controllers with logarithmic quantization. The control scheme can greatly reduce control cost and save both communication channels and bandwidth. By using multiple Lyapunov functions and convex combination techniques, sufficient conditions formulated by a set of linear matrix inequalities are derived to guarantee that all the node systems are synchronized with an isolated trajectory in a finite settling time...
September 18, 2017: IEEE Transactions on Cybernetics
Xiao Ling Wang, Housheng Su, Michael Z Q Chen, Xiao Fan Wang, Guanrong Chen
In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus...
September 18, 2017: IEEE Transactions on Cybernetics
Peipei Li, Lu He, Haiyan Wang, Xuegang Hu, Yuhong Zhang, Lei Li, Xindong Wu
Short text streams such as search snippets and micro blogs have been popular on the Web with the emergence of social media. Unlike traditional normal text streams, these data present the characteristics of short length, weak signal, high volume, high velocity, topic drift, etc. Short text stream classification is hence a very challenging and significant task. However, this challenge has received little attention from the research community. Therefore, a new feature extension approach is proposed for short text stream classification with the help of a large-scale semantic network obtained from a Web corpus...
September 18, 2017: IEEE Transactions on Cybernetics
Luping Ji, Yan Ren, Guisong Liu, Xiaorong Pu
Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns...
September 18, 2017: IEEE Transactions on Cybernetics
Dong-Juan Li, Da-Peng Li
In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown dead-zone inputs are nonlinearly embedded into the systems. These properties of the systems will make it very difficult and challenging to construct a stable controller. By introducing a new diffeomorphism coordinate transformation, the controlled system is first transformed into a state-output model...
September 14, 2017: IEEE Transactions on Cybernetics
Tao Zhou, Fanghui Liu, Harish Bhaskar, Jie Yang
In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank...
September 12, 2017: IEEE Transactions on Cybernetics
Xuelong Li, Kang Liu, Yongsheng Dong
Extracting the foreground from a given complex image is an important and challenging problem. Although there have been many methods to perform foreground extraction, most of them are time-consuming, and the trimaps used in the matting step are labeled manually. In this paper, we propose a fast interactive foreground extraction method based on the superpixel GrabCut and image matting. Specifically, we first extract superpixels from a given image and apply GrabCut on them to obtain a raw mask. Due to that the resulting mask border is hard and toothing, we further propose fast and adaptive trimaps (FATs), and construct an FATs-based shared matting for computing a refined mask...
September 12, 2017: IEEE Transactions on Cybernetics
Yi-Qing Zhang, Xiang Li, Athanasios V Vasilakos
Many complex systems can be modeled as temporal networks with time-evolving connections. The influence of their characteristics on epidemic spreading is analyzed in a susceptible-infected-susceptible epidemic model illustrated by the discrete-time Markov chain approach. We develop the analytical epidemic thresholds in terms of the spectral radius of weighted adjacency matrix by averaging temporal networks, e.g., periodic, nonperiodic Markovian networks, and a special nonperiodic non-Markovian network (the link activation network) in time...
September 11, 2017: IEEE Transactions on Cybernetics
Jing Wang, Feng Tian, Hongchuan Yu, Chang Hong Liu, Kun Zhan, Xiao Wang
Non-negative matrix factorization (NMF), a method for finding parts-based representation of non-negative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views for comprehensive and accurate data representations. Moreover, real-world datasets often come with high-dimensional features, which demands the efficiency of low-dimensional representation learning approaches...
September 8, 2017: IEEE Transactions on Cybernetics
Vignesh Narayanan, Sarangapani Jagannathan
In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem...
September 7, 2017: IEEE Transactions on Cybernetics
Bowen Li, Yang Liu, Kit Ian Kou, Li Yu
This paper investigates the disturbance decoupling problem (DDP) of Boolean control networks (BCNs) by event-triggered control. Using the semi-tensor product of matrices, algebraic forms of BCNs can be achieved, based on which, event-triggered controllers are designed to solve the DDP of BCNs. In addition, the DDP of Boolean partial control networks is also derived by event-triggered control. Finally, two illustrative examples demonstrate the effectiveness of proposed methods.
September 6, 2017: IEEE Transactions on Cybernetics
Sung Jin Yoo, Bong Seok Park
This paper addresses a distributed connectivity-preserving synchronized tracking problem of multiple uncertain nonholonomic mobile robots with limited communication ranges. The information of the time-varying leader robot is assumed to be accessible to only a small fraction of follower robots. The main contribution of this paper is to introduce a new distributed nonlinear error surface for dealing with both the synchronized tracking and the preservation of the initial connectivity patterns among nonholonomic robots...
September 6, 2017: IEEE Transactions on Cybernetics
Guanqun Cao, Alexandros Iosifidis, Ke Chen, Moncef Gabbouj
In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using the Rayleigh quotient, which is extensible for multiple views, supervised learning, and nonlinear embeddings. Numerous methods including canonical correlation analysis, partial least square regression, and linear discriminant analysis are studied using specific intrinsic and penalty graphs within the same framework. Nonlinear extensions based on kernels and (deep) neural networks are derived, achieving better performance than the linear ones...
September 6, 2017: IEEE Transactions on Cybernetics
Jingjing Xiao, Rustam Stolkin, Yuqing Gao, Ales Leonardis
This paper presents a novel robust method for single target tracking in RGB-D images, and also contributes a substantial new benchmark dataset for evaluating RGB-D trackers. While a target object's color distribution is reasonably motion-invariant, this is not true for the target's depth distribution, which continually varies as the target moves relative to the camera. It is therefore nontrivial to design target models which can fully exploit (potentially very rich) depth information for target tracking. For this reason, much of the previous RGB-D literature relies on color information for tracking, while exploiting depth information only for occlusion reasoning...
September 6, 2017: IEEE Transactions on Cybernetics
Licheng Wang, Zidong Wang, Qing-Long Han, Guoliang Wei
The synchronization control problem is investigated for a class of discrete-time dynamical networks with packet dropouts via a coding-decoding-based approach. The data is transmitted through digital communication channels and only the sequence of finite coded signals is sent to the controller. A series of mutually independent Bernoulli distributed random variables is utilized to model the packet dropout phenomenon occurring in the transmissions of coded signals. The purpose of the addressed synchronization control problem is to design a suitable coding-decoding procedure for each node, based on which an efficient decoder-based control protocol is developed to guarantee that the closed-loop network achieves the desired synchronization performance...
September 6, 2017: IEEE Transactions on Cybernetics
Qiuzhen Lin, Genmiao Jin, Yueping Ma, Ka-Chun Wong, Carlos A Coello Coello, Jianqiang Li, Jianyong Chen, Jun Zhang
The multiobjective evolutionary algorithm (MOEA) based on decomposition transforms a multiobjective optimization problem into a set of aggregated subproblems and then optimizes them collaboratively. Since these subproblems usually have different degrees of difficulty, resource allocation (RA) strategies have been reported to enhance performance, attempting to dynamically assign proper amounts of computational resources for the solution of each of these subproblems. However, existing schemes for decomposition-based MOEAs fully rely on the relative improvement of the aggregated functions to do this...
September 6, 2017: IEEE Transactions on Cybernetics
Myeongjin Park, Seung-Hoon Lee, Oh-Min Kwon, Alexandre Seuret
This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work is to model them as graphs whose nodes represent the dynamical units. At this time, we take note of the importance of each node in networks. One way, in this paper, is that the closeness-centrality mentioned in the field of social science is grafted onto the CDNs...
September 6, 2017: IEEE Transactions on Cybernetics
Tianwei Zhou, Zhiqiang Zuo, Yijing Wang
This paper mainly focuses on synchronization of controlled drive-response systems under Lurie form through a limited channel. The main contribution of this paper is the quantizer-based triggered methodology proposed based on three coders. By exploring coder structure information and fusing quantization and trigger errors together, this strategy can reduce transmission burden while increase synchronization speed concurrently. And the final synchronization error can be bounded within a predetermined fixed value...
August 29, 2017: IEEE Transactions on Cybernetics
Huarong Zheng, Rudy R Negenborn, Gabriel Lodewijks
Waterborne autonomous guided vessels (waterborne AGVs) moving over open waters experience environmental uncertainties. This paper proposes a novel cost-effective robust distributed control approach for waterborne AGVs. The overall system is uncertain and has independent subsystem dynamics but coupling objectives and state constraints. Waterborne AGVs determine their actions in a parallel way, while still minimizing an overall cost function and respecting coupling constraints robustly by communicating within a neighborhood...
August 29, 2017: IEEE Transactions on Cybernetics
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