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IEEE Transactions on Cybernetics

Xinyi Le, Sijie Chen, Zheng Yan, Juntong Xi
In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner...
October 18, 2017: IEEE Transactions on Cybernetics
Jun-Wei Zhu, Guang-Hong Yang, Wen-An Zhang, Li Yu
This paper studies the observer based fault tolerant tracking control problem for linear multiagent systems with multiple faults and mismatched disturbances. A novel distributed intermediate estimator based fault tolerant tracking protocol is presented. The leader's input is nonzero and unavailable to the followers. By applying a projection technique, the mismatched disturbances are separated into matched and unmatched components. For each node, a tracking error system is established, for which an intermediate estimator driven by the relative output measurements is constructed to estimate the sensor faults and a combined signal of the leader's input, process faults, and matched disturbance component...
October 17, 2017: IEEE Transactions on Cybernetics
Zhenhua Deng, Shu Liang, Yiguang Hong
In this paper, a distributed resource allocation problem with nonsmooth local cost functions is considered, where the interaction among agents is depicted by strongly connected and weight-balanced digraphs. Here the decision variable of each agent is within a local feasibility constraint described as a convex set, and all the decision variables have to satisfy a network resource constraint, which is the sum of available resources. To solve the problem, a distributed continuous-time algorithm is developed by virtue of differentiated projection operations and differential inclusions, and its convergence to the optimal solution is proved via the set-valued LaSalle invariance principle...
October 17, 2017: IEEE Transactions on Cybernetics
Ding Zhai, Liwei An, Jiuxiang Dong, Qingling Zhang
This paper studies the robust stabilization problem for a class of uncertain nonlinear systems with unstable zero dynamics. The considered zero dynamic is not assumed to be input-to-state practically stable and contains nonlinear uncertainties and mismatched external disturbances. A new robust adaptive fuzzy control method is developed by combining H∞ theory with backstepping technique. First, an ideal C¹ virtual control function is designed, which can guarantee the zero dynamic asymptotically stable with a suboptimal H∞ performance...
October 16, 2017: IEEE Transactions on Cybernetics
Changbing Tang, Ang Li, Xiang Li
Weighted vertex cover (WVC), a generalized type of vertex cover, is one of the most important combinatorial optimization problems. In this paper, we provide a novel solution to the WVC problem from the view of network engineering. We model the WVC problem as an asymmetric game on weighted networks, where each vertex is treated as an intelligent rational agent rather than an inanimate one. Under the framework of asymmetric game, we find that strict Nash equilibriums of the asymmetric game are the intermediate states between the WVC states and the minimum WVC (MWVC) states...
October 16, 2017: IEEE Transactions on Cybernetics
Yongduan Song, Ziyun Shen, Liu He, Xiucai Huang
In this paper, we present a neuroadaptive control for a class of uncertain nonlinear strict-feedback systems with full-state constraints and unknown actuation characteristics where the break points of the dead-zone model are considered as time-variant. In order to deal with the modeling uncertainties and the impact of the nonsmooth actuation characteristics, neural networks are utilized at each step of the backstepping design. By using barrier Lyapunov function, together with the concept of virtual parameter, we develop a neuroadaptive control scheme ensuring tracking stability and at the same time maintaining full-state constraints...
October 13, 2017: IEEE Transactions on Cybernetics
Zhaoxiang Zhang, Jiaxin Chen, Qiang Wu, Ling Shao
Remote person identification by gait is one of the most important topics in the field of computer vision and pattern recognition. However, gait recognition suffers severely from the appearance variance caused by the view change. It is very common that gait recognition has a high performance when the view is fixed but the performance will have a sharp decrease when the view variance becomes significant. Existing approaches have tried all kinds of strategies like tensor analysis or view transform models to slow down the trend of performance decrease but still have potential for further improvement...
October 13, 2017: IEEE Transactions on Cybernetics
Jianhui Wang, Zhi Liu, C L Philip Chen, Yun Zhang
Hysteresis exists ubiquitously in physical actuators. Besides, actuator failures/faults may also occur in practice. Both effects would deteriorate the transient tracking performance, and even trigger instability. In this paper, we consider the problem of compensating for actuator failures and input hysteresis by proposing a fuzzy control scheme for stochastic nonlinear systems. Compared with the existing research on stochastic nonlinear uncertain systems, it is found that how to guarantee a prescribed transient tracking performance when taking into account actuator failures and hysteresis simultaneously also remains to be answered...
October 12, 2017: IEEE Transactions on Cybernetics
Plamen P Angelov, Xiaowei Gu, Jose C Pr
Based on a critical analysis of data analytics and its foundations, we propose a functional approach to estimate data ensemble properties, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space and hence named empirical data analysis (EDA). The ensemble functions include the nonparametric square centrality (a measure of closeness used in graph theory) and typicality (an empirically derived quantity which resembles probability)...
October 12, 2017: IEEE Transactions on Cybernetics
Chaoxu Mu, Ding Wang, Haibo He
This paper presents a data-based finite-horizon optimal control approach for discrete-time nonlinear affine systems. The iterative adaptive dynamic programming (ADP) is used to approximately solve Hamilton-Jacobi-Bellman equation by minimizing the cost function in finite time. The idea is implemented with the heuristic dynamic programming (HDP) involved the model network, which makes the iterative control at the first step can be obtained without the system function, meanwhile the action network is used to obtain the approximate optimal control law and the critic network is utilized for approximating the optimal cost function...
October 10, 2017: IEEE Transactions on Cybernetics
Honggui Han, Wei Lu, Lu Zhang, Junfei Qiao
An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO...
October 9, 2017: IEEE Transactions on Cybernetics
Zhihui Lai, Dongmei Mo, Wai Keung Wong, Yong Xu, Duoqian Miao, David Zhang
Ridge regression (RR) and its extended versions are widely used as an effective feature extraction method in pattern recognition. However, the RR-based methods are sensitive to the variations of data and can learn only limited number of projections for feature extraction and recognition. To address these problems, we propose a new method called robust discriminant regression (RDR) for feature extraction. In order to enhance the robustness, the L₂,₁-norm is used as the basic metric in the proposed RDR. The designed robust objective function in regression form can be solved by an iterative algorithm containing an eigenfunction, through which the optimal orthogonal projections of RDR can be obtained by eigen decomposition...
October 9, 2017: IEEE Transactions on Cybernetics
Deming Yuan, Daniel W C Ho, Guo-Ping Jiang
In this paper, we consider the problem of solving distributed constrained optimization over a multiagent network that consists of multiple interacting nodes in online setting, where the objective functions of nodes are time-varying and the constraint set is characterized by an inequality. Through introducing a regularized convex-concave function, we present a consensus-based adaptive primal-dual subgradient algorithm that removes the need for knowing the total number of iterations T in advance. We show that the proposed algorithm attains an $O (T1/2 + c) [where c∈(0,1/2)] regret bound and an O (T1 - c/2) bound on the violation of constraints; in addition, we show an improvement to an $O (Tc) regret bound when the objective functions are strongly convex...
October 5, 2017: IEEE Transactions on Cybernetics
Zheng Wang, Ruimin Hu, Chen Chen, Yi Yu, Junjun Jiang, Chao Liang, Shin'ichi Satoh
Person reidentification (re-id), as an important task in video surveillance and forensics applications, has been widely studied. Previous research efforts toward solving the person re-id problem have primarily focused on constructing robust vector description by exploiting appearance's characteristic, or learning discriminative distance metric by labeled vectors. Based on the cognition and identification process of human, we propose a new pattern, which transforms the feature description from characteristic vector to discrepancy matrix...
October 5, 2017: IEEE Transactions on Cybernetics
Jian Han, Huaguang Zhang, Yingchun Wang, Xun Sun
This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method...
October 3, 2017: IEEE Transactions on Cybernetics
Min Gan, C L Philip Chen, Guang-Yong Chen, Long Chen
For a class of nonlinear least squares problems, it is usually very beneficial to separate the variables into a linear and a nonlinear part and take full advantage of reliable linear least squares techniques. Consequently, the original problem is turned into a reduced problem which involves only nonlinear parameters. We consider in this paper four separated algorithms for such problems. The first one is the variable projection (VP) algorithm with full Jacobian matrix of Golub and Pereyra. The second and third ones are VP algorithms with simplified Jacobian matrices proposed by Kaufman and Ruano et al...
October 3, 2017: IEEE Transactions on Cybernetics
Minyu Feng, Hong Qu, Zhang Yi, Jurgen Kurths
During the past decades, power-law distributions have played a significant role in analyzing the topology of scale-free networks. However, in the observation of degree distributions in practical networks and other nonuniform distributions such as the wealth distribution, we discover that, there exists a peak at the beginning of most real distributions, which cannot be accurately described by a monotonic decreasing power-law distribution. To better describe the real distributions, in this paper, we propose a subnormal distribution derived from evolving networks with variable elements and study its statistical properties for the first time...
October 2, 2017: IEEE Transactions on Cybernetics
Min Meng, Lu Liu, Gang Feng
This paper investigates the adaptive output regulation problem for heterogeneous linear multiagent systems under randomly switching communication topologies. The switching mechanism is governed by a time-homogeneous Markov process, whose states correspond to all possible communication topologies among agents. A novel distributed adaptive cooperative controller is presented, where the dynamic compensators are utilized to estimate the exogenous signal for all the agents in mean square sense. The distributed control law is based upon the local information of agents, without using the global information of the communication topologies...
September 29, 2017: IEEE Transactions on Cybernetics
Youxi Wu, Yao Tong, Xingquan Zhu, Xindong Wu
Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover meaningful subsequences suitable for their own application domains, such as finding gene transcription sites from DNA sequences or discovering patterns for time series data classification. Due to the inherent complexity of sequence patterns, including the exponential candidate space with respect to pattern letters and gap constraints, to date, existing sequence pattern mining methods are either incomplete or do not support the Apriori property because the support ratio of a pattern may be greater than that of its subpatterns...
September 28, 2017: IEEE Transactions on Cybernetics
Joanna Turner, Qinggang Meng, Gerald Schaefer, Amanda Whitbrook, Andrea Soltoggio
This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks...
September 28, 2017: IEEE Transactions on Cybernetics
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