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IEEE Transactions on Cybernetics

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https://www.readbyqxmd.com/read/28809720/aperiodic-robust-model-predictive-control-for-constrained-continuous-time-nonlinear-systems-an-event-triggered-approach
#1
Changxin Liu, Jian Gao, Huiping Li, Demin Xu
The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC...
August 14, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28809721/pose-prediction-based-opportunistic-sensing-for-energy-efficiency-in-sensor-networks-using-distributed-supervisors
#2
James Z Hare, Shalabh Gupta, Thomas A Wettergren
This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent...
August 11, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28796628/a-random-walk-approach-to-query-informative-constraints-for-clustering
#3
Ahmad Ali Abin
This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph, the more similar those nodes are. Since computing the commute time takes the Laplacian eigenspectrum into account, we use this property in a recursive fashion to query informative constraints for clustering...
August 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28792909/set-based-discrete-particle-swarm-optimization-based-on-decomposition-for-permutation-based-multiobjective-combinatorial-optimization-problems
#4
Xue Yu, Wei-Neng Chen, Tianlong Gu, Huaxiang Zhang, Huaqiang Yuan, Sam Kwong, Jun Zhang
This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D...
August 7, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28792910/semantic-feature-learning-for-heterogeneous-multitask-classification-via-non-negative-matrix-factorization
#5
Fuzhen Zhuang, Xuebing Li, Xin Jin, Dapeng Zhang, Lirong Qiu, Qing He
Multitask learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve the generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous features for each task. To address this problem, we first construct an integrated graph of a set of bipartite graphs to build a connection among different tasks...
August 3, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28792908/nonrigid-point-set-registration-by-preserving-local-connectivity
#6
Lifei Bai, Xianqiang Yang, Huijun Gao
This paper is concerned with the nonrigid point set registration problem and a probability-based registration algorithm with local connectivity preservation is proposed. A unified formulation for point set registration problem is introduced and the derived energy function is composed of three parts, distance measurement item, transformation constraint item, and correspondence constraint item. In order to preserve the local structure of point set, the definitions of k-connected neighbors and connectivity matrix are given and the local connectivity constraint is constructed as a weighted least square error item...
August 3, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28783655/a-flexible-terminal-approach-to-sampled-data-exponentially-synchronization-of-markovian-neural-networks-with-time-varying-delayed-signals
#7
Jun Cheng, Ju H Park, Hamid Reza Karimi, Hao Shen
This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality...
August 2, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28783654/discriminative-transformation-learning-for-fuzzy-sparse-subspace-clustering
#8
Zaidao Wen, Biao Hou, Qian Wu, Licheng Jiao
This paper develops a novel iterative framework for subspace clustering (SC) in a learned discriminative feature domain. This framework consists of two modules of fuzzy sparse SC and discriminative transformation learning. In the first module, fuzzy latent labels containing discriminative information and latent representations capturing the subspace structure will be simultaneously evaluated in a feature domain. Then the linear transforming operator with respect to the feature domain will be successively updated in the second module with the advantages of more discrimination, subspace structure preservation, and robustness to outliers...
August 2, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28783653/cooperative-output-regulation-of-lti-plant-via-distributed-observers-with-local-measurement
#9
Kexin Liu, Yao Chen, Zhisheng Duan, Jinhu Lu
Over the last decades, distributed output regulation problems have received much consideration due to its extensively applications in real world practices. Traditionally, it is assumed that each node obtains the same signal. However, an important observation is that each agent possesses different measurement due to the observability or configuration of the systems. To solve the output regulation problem in this case, we proposed a cooperative output regulation network, where each agent obtains a part of system output measurement on states of plant and exosystem...
August 2, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767384/distributed-differential-evolution-based-on-adaptive-mergence-and-split-for-large-scale-optimization
#10
Yong-Feng Ge, Wei-Jie Yu, Ying Lin, Yue-Jiao Gong, Zhi-Hui Zhan, Wei-Neng Chen, Jun Zhang
Nowadays, large-scale optimization problems are ubiquitous in many research fields. To deal with such problems efficiently, this paper proposes a distributed differential evolution with adaptive mergence and split (DDE-AMS) on subpopulations. The novel mergence and split operators are designed to make full use of limited population resource, which is important for large-scale optimization. They are adaptively performed based on the performance of the subpopulations. During the evolution, once a subpopulation finds a promising region, the current worst performing subpopulation will merge into it...
July 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767383/privacy-preservation-in-distributed-subgradient-optimization-algorithms
#11
Youcheng Lou, Lean Yu, Shouyang Wang, Peng Yi
In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered. Most of the existing distributed algorithms focus mainly on the algorithm design and convergence analysis, but not the protection of agents' privacy. Privacy is becoming an increasingly important issue in applications involving sensitive information. In this paper, we first show that the distributed subgradient synchronous homogeneous-stepsize algorithm is not privacy preserving in the sense that the malicious agent can asymptotically discover other agents' subgradients by transmitting untrue estimates to its neighbors...
July 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767380/delay-dependent-functional-observer-design-for-linear-systems-with-unknown-time-varying-state-delays
#12
Reza Mohajerpoor, Lakshmanan Shanmugam, Hamid Abdi, Saeid Nahavandi, Ju H Park
Partial state estimation has numerous applications in practice. Nevertheless, designing delay-dependent functional observers (FOs) for systems with unknown time delays is rigorous and still an open dilemma. This paper addresses the problem for linear time-invariant systems with state time-varying delays. The delay is assumed to be bounded in an interval with a bounded derivative. A sliding mode FO structure that is robust against the delay uncertainties is established to this aim. The structure employs an auxiliary delay function that can be defined based on the existing knowledge on the actual delay values...
July 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767382/output-feedback-control-and-stabilization-for-multiplicative-noise-systems-with-intermittent-observations
#13
Qingyuan Qi, Huanshui Zhang
This paper mainly focuses on the optimal output feedback control and stabilization problems for discrete-time multiplicative noise system with intermittent observations. The main contributions of this paper can be concluded as follows. First, different from the previous literatures, this paper overcomes the barrier of the celebrated separation principle for stochastic control problems of multiplicative noise systems. Based on the measurement process, the optimal estimation is presented, and by using dynamic programming principle, the optimal output feedback controller is designed with feedback gain based on the given coupled Riccati equations...
July 28, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767381/towards-occlusion-handling-object-tracking-with-background-estimation
#14
Sicong Zhao, Shunli Zhang, Li Zhang
The appearance model of the target needs to be updated for online single object tracking. However, the variation of the observation can be caused by active appearance change of the target, or the occlusion from the background. For the former case, we should update the appearance model and for the latter, the current model should be preserved. In this paper, we distinguish these two cases and resist the impact from heavy occlusion by estimating the background in the scene with moving cameras, while retaining the adaptivity to stationary cameras at the same time...
July 27, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767379/composite-backstepping-consensus-algorithms-of-leader-follower-higher-order-nonlinear-multiagent-systems-subject-to-mismatched-disturbances
#15
Xiangyu Wang, Shihua Li, Michael Z Q Chen
This paper is devoted to solving the output consensus problem of leader-follower higher-order nonlinear multiagent systems subject to mismatched disturbances. The disturbances are allowed to be in higher-order forms. First, by constructing a generalized proportional-integral observer for each follower, estimates of the disturbances and their derivatives are obtained. At the same time, a distributed observer is also developed for the followers to estimate the leader state information. Second, based on the estimates of the disturbances and the leader state, together with the backstepping technique, a feedforward-feedback composite consensus control scheme is proposed...
July 27, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767378/adaptive-neural-network-control-of-a-robotic-manipulator-with-time-varying-output-constraints
#16
Wei He, Haifeng Huang, Shuzhi Sam Ge
The control problem of an uncertain n-degrees of freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid robotic manipulator system as a multi-input and multi-output nonlinear system. We devise a disturbance observer to estimate the unknown disturbance from humans and environment. To solve the uncertain problem, a neural network which utilizes a radial basis function is used to estimate the unknown dynamics of the robotic manipulator. An asymmetric barrier Lyapunov function is employed in the process of control design to avert the contravention of the time-varying output constraints...
July 27, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28767377/denoising-hyperspectral-image-with-non-i-i-d-noise-structure
#17
Yang Chen, Xiangyong Cao, Qian Zhao, Deyu Meng, Zongben Xu
Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods...
July 27, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28749366/robust-learning-with-kernel-mean-p-power-error-loss
#18
Badong Chen, Lei Xing, Xin Wang, Jing Qin, Nanning Zheng
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE...
July 25, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28749365/discriminative-joint-feature-topic-model-with-dual-constraints-for-wce-classification
#19
Yixuan Yuan, Xiwen Yao, Junwei Han, Lei Guo, Max Q-H Meng
Wireless capsule endoscopy (WCE) enables clinicians to examine the digestive tract without any surgical operations, at the cost of a large amount of images to be analyzed. The main challenge for automatic computer-aided diagnosis arises from the difficulty of robust characterization of these images. To tackle this problem, a novel discriminative joint-feature topic model (DJTM) with dual constraints is proposed to classify multiple abnormalities in WCE images. We first propose a joint-feature probabilistic latent semantic analysis (PLSA) model, where color and texture descriptors extracted from same image patches are jointly modeled with their conditional distributions...
July 25, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28749364/bayesian-random-vector-functional-link-networks-for-robust-data-modeling
#20
Simone Scardapane, Dianhui Wang, Aurelio Uncini
Random vector functional-link (RVFL) networks are randomized multilayer perceptrons with a single hidden layer and a linear output layer, which can be trained by solving a linear modeling problem. In particular, they are generally trained using a closed-form solution of the (regularized) least-squares approach. This paper introduces several alternative strategies for performing full Bayesian inference (BI) of RVFL networks. Distinct from standard or classical approaches, our proposed Bayesian training algorithms allow to derive an entire probability distribution over the optimal output weights of the network, instead of a single pointwise estimate according to some given criterion (e...
July 25, 2017: IEEE Transactions on Cybernetics
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