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IEEE Transactions on Cybernetics

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https://www.readbyqxmd.com/read/28422679/a-bio-inspired-approach-to-traffic-network-equilibrium-assignment-problem
#1
Xiaoge Zhang, Sankaran Mahadevan
Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes...
April 14, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422676/supervised-and-unsupervised-aspect-category-detection-for-sentiment-analysis-with-co-occurrence-data
#2
Kim Schouten, Onne van der Weijde, Flavius Frasincar, Rommert Dekker
Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods...
April 14, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422674/incorporation-of-efficient-second-order-solvers-into-latent-factor-models-for-accurate-prediction-of-missing-qos-data
#3
Xin Luo, MengChu Zhou, Shuai Li, YunNi Xia, Zhu-Hong You, QingSheng Zhu, Hareton Leung
Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector...
April 14, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422673/a-two-phase-evolutionary-approach-for-compressive-sensing-reconstruction
#4
Yu Zhou, Sam Kwong, Hainan Guo, Xiao Zhang, Qingfu Zhang
Sparse signal reconstruction can be regarded as a problem of locating the nonzero entries of the signal. In presence of measurement noise, conventional methods such as l₁ norm relaxation methods and greedy algorithms, have shown their weakness in finding the nonzero entries accurately. In order to reduce the impact of noise and better locate the nonzero entries, in this paper, we propose a two-phase algorithm which works in a coarse-to-fine manner. In phase 1, a decomposition-based multiobjective evolutionary algorithm is applied to generate a group of robust solutions by optimizing l₁ norm of the solutions...
April 14, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422678/correlation-filter-learning-toward-peak-strength-for-visual-tracking
#5
Yao Sui, Guanghui Wang, Li Zhang
This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering...
April 13, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422677/learning-sparse-representation-for-objective-image-retargeting-quality-assessment
#6
Qiuping Jiang, Feng Shao, Weisi Lin, Gangyi Jiang
The goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment...
April 13, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422675/finite-time-synchronization-of-coupled-hierarchical-hybrid-neural-networks-with-time-varying-delays
#7
Junyi Wang, Huaguang Zhang, Zhanshan Wang, David Wenzhong Gao
This paper is concerned with the finite-time synchronization problem of coupled hierarchical hybrid delayed neural networks. This coupled hierarchical hybrid neural networks consist of a higher level switching and a lower level Markovian jumping. The time-varying delays are dependent on not only switching signal but also jumping mode. By using a less conservative weighted integral inequality and stochastic multiple Lyapunov-Krasovskii functional, new finite-time synchronization criteria are obtained, which makes the state trajectories be kept within the prescribed bound in a time interval...
April 13, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422702/neuronal-state-estimation-for-neural-networks-with-two-additive-time-varying-delay-components
#8
Xian-Ming Zhang, Qing-Long Han, Zidong Wang, Bao-Lin Zhang
This paper is concerned with the state estimation for neural networks with two additive time-varying delay components. Three cases of these two time-varying delays are fully considered: 1) both delays are differentiable uniformly bounded with delay-derivative bounded by some constants; 2) one delay is continuous uniformly bounded while the other is differentiable uniformly bounded with delay-derivative bounded by certain constants; and 3) both delays are continuous uniformly bounded. First, an extended reciprocally convex inequality is introduced to bound reciprocally convex combinations appearing in the derivative of some Lyapunov-Krasovskii functional...
April 12, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422701/aperiodic-optimal-linear-estimation-for-networked-systems-with-communication-uncertainties
#9
Wen-An Zhang, Michael Z Q Chen, Andong Liu, Steven Liu
The aperiodic optimal linear estimator design problem is investigated in this paper for networked systems with communication uncertainties, including delays and data losses, where the sampling and estimation are nonuniform and asynchronous. Based on the idea of measurement fusion, two approaches are proposed to design the aperiodic estimators, and it is shown that the estimator is equivalent to that designed by the measurement augmentation method in performance. Moreover, the estimation performance is improved by using a newly proposed measurement retransmission scheme as compared with the commonly used hold-input and zero-input schemes, by which the lost measurements are never used once they are lost...
April 12, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28422700/adaptive-fuzzy-control-for-nonstrict-feedback-systems-with-unmodeled-dynamics-and-fuzzy-dead-zone-via-output-feedback
#10
Lijie Wang, Hongyi Li, Qi Zhou, Renquan Lu
This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded...
April 12, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28410115/networked-predictive-control-for-nonlinear-systems-with-arbitrary-region-quantizers
#11
Hongjiu Yang, Yang Xu, Yuanqing Xia, Jinhui Zhang
In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization...
April 6, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28410114/multilayer-optimization-of-heterogeneous-networks-using-grammatical-genetic-programming
#12
Michael Fenton, David Lynch, Stepan Kucera, Holger Claussen, Michael O'Neill
Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs...
April 6, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28391215/fast-variable-structure-stochastic-automaton-for-discovering-and-tracking-spatiotemporal-event-patterns
#13
Junqi Zhang, Yuheng Wang, Cheng Wang, MengChu Zhou
Discovering and tracking spatiotemporal event patterns have many applications. For example, in a smart-home project, a set of spatiotemporal pattern learning automata are used to monitor a user's repetitive activities, by which the home's automaticity can be promoted while some of his/her burdens can be reduced. Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic automata whose state transition function is fixed and predesigned to guarantee their immunity to noise...
April 5, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28391217/effects-of-preview-on-human-control-behavior-in-tracking-tasks-with-various-controlled-elements
#14
Kasper van der El, Daan M Pool, Marinus M van Paassen, Max Mulder
This paper investigates how humans use a previewed target trajectory for control in tracking tasks with various controlled element dynamics. The human's hypothesized "near" and "far" control mechanisms are first analyzed offline in simulations with a quasi-linear model. Second, human control behavior is quantified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects tracked identical target trajectories with a pursuit and a preview display, each with gain, single-, and double-integrator controlled element dynamics...
April 4, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28391216/faroc-fast-and-robust-supervised-canonical-correlation-analysis-for-multimodal-omics-data
#15
Ankita Mandal, Pradipta Maji
One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features...
April 4, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28391218/analysis-and-design-of-synchronization-for-heterogeneous-network
#16
Yuanqing Wu, Renquan Lu, Peng Shi, Hongye Su, Zheng-Guang Wu
In this paper, we investigate the synchronization for heterogeneous network subject to event-triggering communication. The designed controller for each node includes reference generator (RG) and regulator. The predicted value of relative information between intermittent communication can significantly reduce the transmitted information. Based on the event triggering strategy and time-dependent threshold, all RGs can exponentially track the target trajectory. Then by the action of regulator, each node synchronizes with its RG...
April 3, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28371795/cooperative-hierarchical-pso-with-two-stage-variable-interaction-reconstruction-for-large-scale-optimization
#17
Hongwei Ge, Liang Sun, Guozhen Tan, Zheng Chen, C L Philip Chen
Large scale optimization problems arise in diverse fields. Decomposing the large scale problem into small scale subproblems regarding the variable interactions and optimizing them cooperatively are critical steps in an optimization algorithm. To explore the variable interactions and perform the problem decomposition tasks, we develop a two stage variable interaction reconstruction algorithm. A learning model is proposed to explore part of the variable interactions as prior knowledge. A marginalized denoising model is proposed to construct the overall variable interactions using the prior knowledge, with which the problem is decomposed into small scale modules...
March 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28371794/finite-horizon-h%C3%A2-consensus-control-of-time-varying-multiagent-systems-with-stochastic-communication-protocol
#18
Lei Zou, Zidong Wang, Huijun Gao, Fuad E Alsaadi
This paper is concerned with the distributed H∞ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system...
March 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28371792/a-barycentric-coordinate-based-approach-to-formation-control-under-directed-and-switching-sensing-graphs
#19
Tingrui Han, Zhiyun Lin, Ronghao Zheng, Minyue Fu
This paper investigates two formation control problems for a leader-follower network in 3-D. One is called the formation marching control problem, the objective of which is to steer the agents to maintain a target formation shape while moving with the synchronized velocity. The other one is called the formation rotating control problem, whose goal is to drive the agents to rotate around a common axis with a target formation. For the above two problems, we consider directed and switching sensing topologies while the communication is assumed to be bidirectional and switching...
March 31, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28371791/adaptive-neural-tracking-control-for-switched-high-order-stochastic-nonlinear-systems
#20
Xudong Zhao, Xinyong Wang, Guangdeng Zong, Xiaolong Zheng
This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems...
March 31, 2017: IEEE Transactions on Cybernetics
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