• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-25
Meijun Sun; Ziqi Zhou; Qinghua Hu; Zheng Wang; Jianmin Jiang

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

更新日期：2018-05-27
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-25
Yi Bin; Yang Yang; Fumin Shen; Ning Xie; Heng Tao Shen; Xuelong Li

Video captioning has been attracting broad research attention in the multimedia community. However, most existing approaches heavily rely on static visual information or partially capture the local temporal knowledge (e.g., within 16 frames), thus hardly describing motions accurately from a global view. In this paper, we propose a novel video captioning framework, which integrates bidirectional long-short term memory (BiLSTM) and a soft attention mechanism to generate better global representations for videos as well as enhance the recognition of lasting motions in videos. To generate video captions, we exploit another long-short term memory as a decoder to fully explore global contextual information. The benefits of our proposed method are two fold: 1) the BiLSTM structure comprehensively preserves global temporal and visual information and 2) the soft attention mechanism enables a language decoder to recognize and focus on principle targets from the complex content. We verify the effectiveness of our proposed video captioning framework on two widely used benchmarks, that is, microsoft video description corpus and MSR-video to text, and the experimental results demonstrate the superiority of the proposed approach compared to several state-of-the-art methods.

更新日期：2018-05-27
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-23
Shiqi Zheng; Wenjie Li

This paper concentrates on the study of finite time control for nonlinear switched systems. Based on a newly introduced adding a barrier integrator technique, a novel adaptive fuzzy control strategy is proposed for a class of nonlinear switched systems. Compared with the existing adaptive control methods, the proposed method has several distinguishing features. Finite time control: the proposed adaptive control method can solve the exact finite time control problem for the stabilization and some types of tracking issues. Namely, the errors will converge to zero in finite time. For a general tracking problem, the practical finite time control can be achieved. More general systems: the proposed method is suitable for high order nonlinear switched systems with arbitrary switching and unknown control gains. Some strict assumptions on the system dynamics are relaxed. Full state constraints: the proposed method can be utilized to deal with the full state constraints problem. Simple controller structure: the explosion of the complexity'' in the backstepping design is avoided. Singularity free design: the singularity problem is carefully handled during the whole design procedure. Examples are presented to illustrate the effectiveness of the proposed method.

更新日期：2018-05-24
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-22
Weiping Ding; Chin-Teng Lin; Zehong Cao

Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.

更新日期：2018-05-23
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-22
Bin Sheng; Ping Li; Shuangjia Mo; Huating Li; Xuhong Hou; Qiang Wu; Jing Qin; Ruogu Fang; David Dagan Feng

The retinal vessel is one of the determining factors in an ophthalmic examination. Automatic extraction of retinal vessels from low-quality retinal images still remains a challenging problem. In this paper, we propose a robust and effective approach that qualitatively improves the detection of low-contrast and narrow vessels. Rather than using the pixel grid, we use a superpixel as the elementary unit of our vessel segmentation scheme. We regularize this scheme by combining the geometrical structure, texture, color, and space information in the superpixel graph. And the segmentation results are then refined by employing the efficient minimum spanning superpixel tree to detect and capture both global and local structure of the retinal images. Such an effective and structure-aware tree detector significantly improves the detection around the pathologic area. Experimental results have shown that the proposed technique achieves advantageous connectivity-area-length (CAL) scores of 80.92% and 69.06% on two public datasets, namely, DRIVE and STARE, thereby outperforming state-of-the-art segmentation methods. In addition, the tests on the challenging retinal image database have further demonstrated the effectiveness of our method. Our approach achieves satisfactory segmentation performance in comparison with state-of-the-art methods. Our technique provides an automated method for effectively extracting the vessel from fundus images.

更新日期：2018-05-23
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-22
Weifeng Liu; Xueqi Ma; Yicong Zhou; Dapeng Tao; Jun Cheng

The explosive growth of multimedia data on the Internet makes it essential to develop innovative machine learning algorithms for practical applications especially where only a small number of labeled samples are available. Manifold regularized semi-supervised learning (MRSSL) thus received intensive attention recently because it successfully exploits the local structure of data distribution including both labeled and unlabeled samples to leverage the generalization ability of a learning model. Although there are many representative works in MRSSL, including Laplacian regularization (LapR) and Hessian regularization, how to explore and exploit the local geometry of data manifold is still a challenging problem. In this paper, we introduce a fully efficient approximation algorithm of graph p-Laplacian, which significantly saving the computing cost. And then we propose p-LapR (pLapR) to preserve the local geometry. Specifically, p-Laplacian is a natural generalization of the standard graph Laplacian and provides convincing theoretical evidence to better preserve the local structure. We apply pLapR to support vector machines and kernel least squares and conduct the implementations for scene recognition. Extensive experiments on the Scene 67 dataset, Scene 15 dataset, and UC-Merced dataset validate the effectiveness of pLapR in comparison to the conventional manifold regularization methods.

更新日期：2018-05-23
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-21
Shiwen Xie; Yongfang Xie; Tingwen Huang; Weihua Gui; Chunhua Yang

In the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process. This paper proposes a coordinated optimization strategy for setting the descent gradient of OFIC. First, an optimal setting module is established to determine the initial set-points of the descent gradient. The oxygen utilization ratio (OUR), an important parameter in this module, cannot be measured online. Therefore, a self-adjusting RBF (SARBF) neural network with an adaptive learning rate is developed to estimate the OUR. The convergence of the SARBF neural network is discussed. Then, a coordinated optimization strategy is proposed to adjust the set-points of the descent gradient when the measured OFICs drift away from their desired set-pints. If the final OFIC does not satisfy the process requirements, a compensation mechanism is developed to provide a compensation for the set-points of the descent gradient. Finally, industrial experiments in the largest zinc hydrometallurgy plant validate the effectiveness of the proposed coordinated optimization strategy. Our strategy improves the qualified ratio of the OFIC and the quality of the goethite precipitate. More profit is created to the iron removal process after our strategy is applied.

更新日期：2018-05-22
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-21
Weidong Zou; Yuanqing Xia; Huifang Li

Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.

更新日期：2018-05-22
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-21
Hao Zhang; Bo Chen; Zhengjue Wang; Hongwei Liu

In this paper, a unified Bayesian max-margin discriminant projection framework is proposed, which is able to jointly learn the discriminant feature space and the max-margin classifier with different relationships between the latent representations and observations. We assume that the latent representation follows a normal distribution whose sufficient statistics are functions of the observations. The function can be flexibly realized through either shallow or deep structures. The shallow structure includes linear, nonlinear kernel-based functions, and even the convolutional projection, which can be further trained layerwisely to build a multilayered convolutional feature learning model. To take the advantage of the deep neural networks, especially their highly expressive ability and efficient parameter learning, we integrate Bayesian modeling and the popular neural networks, for example, mltilayer perceptron and convolutional neural network, to build an end-to-end Bayesian deep discriminant projection under the proposed framework, which degenerated into the existing shallow linear or convolutional projection with the single-layer structure. Moreover, efficient scalable inferences for the realizations with different functions are derived to handle large-scale data via a stochastic gradient Markov chain Monte Carlo. Finally, we demonstrate the effectiveness and efficiency of the proposed models by the experiments on real-world data, including four image benchmarks (MNIST, CIFAR-10, STL-10, and SVHN) and one measured radar high-resolution range profile dataset, with the detailed analysis about the parameters and computational complexity.

更新日期：2018-05-22
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-21
Xiangzhi Bai; Yuxuan Zhang; Haonan Liu; Zhiguo Chen

Magnetic resonance imaging (MRI) is extensively applied in clinical practice. Segmentation of the MRI brain image is significant to the detection of brain abnormalities. However, owing to the coexistence of intensity inhomogeneity and noise, dividing the MRI brain image into different clusters precisely has become an arduous task. In this paper, an improved possibilistic fuzzy c-means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images. By introducing the new similarity measure, the proposed method is more effective for clustering the data with nonspherical distribution. Besides that, the new similarity measure could alleviate the cluster-size sensitivity'' problem that most FCM-based methods suffer from. Simultaneously, the proposed method could preserve image details as well as suppress image noises via the use of local label information. Experiments conducted on both synthetic and clinical images show that the proposed method is very effective, providing mitigation to the cluster-size sensitivity problem, resistance to noisy images, and applicability to data with more complex distribution.

更新日期：2018-05-22
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-18
Derui Ding; Zidong Wang; Qing-Long Han; Guoliang Wei

The neural-network (NN)-based output-feedback control is considered for a class of stochastic nonlinear systems under round-Robin (RR) scheduling protocols. For the purpose of effectively mitigating data congestions and saving energies, the RR protocols are implemented and the resulting nonlinear systems become the so-called protocol-induced periodic ones. Taking such a periodic characteristic into account, an NN-based observer is first proposed to reconstruct the system states where a novel adaptive tuning law on NN weights is adopted to cater to the requirement of performance analysis. In addition, with the established boundedness of the periodic systems in the mean-square sense, the desired observer gain is obtained by solving a set of matrix inequalities. Then, an actor-critic NN scheme with a time-varying step length in adaptive law is developed to handle the considered control problem with terminal constraints over finite-horizon. Some sufficient conditions are derived to guarantee the boundedness of estimation errors of critic and actor NN weights. In view of these conditions, some key parameters in adaptive tuning laws are easily determined via elementary algebraic operations. Furthermore, the stability in the mean-square sense is investigated for the discussed issue in infinite horizon. Finally, a simulation example is utilized to illustrate the applicability of the proposed control scheme.

更新日期：2018-05-19
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-18
Zong-Gan Chen; Zhi-Hui Zhan; Ying Lin; Yue-Jiao Gong; Tian-Long Gu; Feng Zhao; Hua-Qiang Yuan; Xiaofeng Chen; Qing Li; Jun Zhang

Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

更新日期：2018-05-19
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15

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更新日期：2018-05-18
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15

更新日期：2018-05-18
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15

Provides a listing of current committee members and society officers.

更新日期：2018-05-18
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-16
Xulun Ye; Jieyu Zhao; Long Zhang; Lijun Guo

Multimanifold clustering separates data points approximately lying on a union of submanifolds into several clusters. In this paper, we propose a new nonparametric Bayesian model to handle the manifold data structure. In our framework, we first model the manifold mapping function between Euclidean space and topological space by applying a deep neural network, and then construct the corresponding generation process of multiple manifold data. To solve the posterior approximation problem, in the optimization procedure, we apply a variational auto-encoder-based optimization algorithm. Especially, as the manifold algorithm has poor performance on the real dataset where nonmanifold and manifold clusters are appearing simultaneously, we expand our proposed manifold algorithm by integrating it with the original Dirichlet process mixture model. Experimental results have been carried out to demonstrate the state-of-the-art clustering performance.

更新日期：2018-05-17
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-16
Qichao Zhang; Dongbin Zhao

This paper is concerned about the nonlinear optimization problem of nonzero-sum (NZS) games with unknown drift dynamics. The data-based integral reinforcement learning (IRL) method is proposed to approximate the Nash equilibrium of NZS games iteratively. Furthermore, we prove that the data-based IRL method is equivalent to the model-based policy iteration algorithm, which guarantees the convergence of the proposed method. For the implementation purpose, a single-critic neural network structure for the NZS games is given. To enhance the application capability of the data-based IRL method, we design the updating laws of critic weights based on the offline and online iterative learning methods, respectively. Note that the experience replay technique is introduced in the online iterative learning, which can improve the convergence rate of critic weights during the learning process. The uniform ultimate boundedness of the critic weights are guaranteed using the Lyapunov method. Finally, the numerical results demonstrate the effectiveness of the data-based IRL algorithm for nonlinear NZS games with unknown drift dynamics.

更新日期：2018-05-17
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-16
Baoquan Li; Xuebo Zhang; Yongchun Fang; Wuxi Shi

This paper proposes a novel monocular visual servoing strategy, which can drive a wheeled mobile robot to the desired pose without a prerecorded desired image. Compared with existing methods that adopt the teaching pattern for visual regulation, this scheme can still work well in the situation that the desired image has not been previously acquired. Thus, with the aid of this method, it is more convenient for mobile robots to execute visual servoing tasks. Specifically, to deal with nonexistence of the desired image, the reference frame is craftily defined by taking advantage of visual targets and the planar motion constraint, and the pose estimation algorithm is designed for the mobile robot with respect to the reference frame. Then, an adaptive visual regulation controller is developed to drive the mobile robot to the intermediate frame, where the parameter updating law is constructed for the unknown feature height based on the concurrent learning framework. Stability analysis shows that regulation errors and height identification error can converge simultaneously. Afterwards, the mobile robot is driven to the metric desired pose with the identified feature height. Both simulation and experimental results are provided to validate the performance of this strategy.

更新日期：2018-05-17
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-20
Xiaoming Zhang; Senzhang Wang; Zhoujun Li; Shuai Ma

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-09-22
Peibo Duan; Changsheng Zhang; Guoqiang Mao; Bin Zhang

User association has emerged as a distributed resource allocation problem in the heterogeneous networks (HetNets). Although an approximate solution is obtainable using the approaches like combinatorial optimization and game theory-based schemes, these techniques can be easily trapped in local optima. Furthermore, the lack of exploring the relation between the quality of the solution and the parameters in the HetNet [e.g., the number of users and base stations (BSs)], at what levels, impairs the practicability of deploying these approaches in a real world environment. To address these issues, this paper investigates how to model the problem as a distributed constraint optimization problem (DCOP) from the point of the view of the multiagent system. More specifically, we develop two models named each connection as variable (ECAV) and each BS and user as variable (EBUAV). Hereinafter, we propose a DCOP solver which not only sets up the model in a distributed way but also enables us to efficiently obtain the solution by means of a complete DCOP algorithm based on distributed message-passing. Naturally, both theoretical analysis and simulation show that different qualitative solutions can be obtained in terms of an introduced parameter ${\eta }$ which has a close relation with the parameters in the HetNet. It is also apparent that there is 6% improvement on the throughput by the DCOP solver comparing with other counterparts when ${\eta =3}$ . Particularly, it demonstrates up to 18% increase in the ability to make BSs service more users when the number of users is above 200 while the available resource blocks (RBs) are limited. In addition, it appears that the distribution of RBs allocated to users by BSs is better with the variation of the volume of RBs at the macro BS.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-20
Wensen Feng; Peng Qiao; Yunjin Chen

The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts. However, the straightforward direct gradient descent employed in the original TNRD-based denoising task is not applicable in this paper. To solve this problem, we resort to the proximal gradient descent method. We retrain the model parameters, including the linear filters and influence functions by taking into account the Poisson noise statistics, and end up with a well-trained nonlinear diffusion model specialized for Poisson denoising. The trained model provides strongly competitive results against state-of-the-art approaches, meanwhile bearing the properties of simple structure and high efficiency. Furthermore, our proposed model comes along with an additional advantage, that the diffusion process is well-suited for parallel computation on graphics processing units (GPUs). For images of size $512 \times 512$ , our GPU implementation takes less than 0.1 s to produce state-of-the-art Poisson denoising performance.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Jianhua Dai; Hu Hu; Qinghua Hu; Wei Huang; Nenggan Zheng; Liang Liu

The matrix completion problem is restoring a given matrix with missing entries when handling incomplete data. In many existing researches, rank minimization plays a central role in matrix completion. In this paper, noticing that the locally linear reconstruction can be used to approximate the missing entries, we view the problem from a new perspective and propose an algorithm called locally linear approximation (LLA). The LLA method tries to keep the local structure of the data space while restoring the missing entries from row angle and column angle simultaneously. The experimental results have demonstrated the effectiveness of the proposed method.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-23
Binh Tran; Bing Xue; Mengjie Zhang

In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton–Jacobi–Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Fei Chen; Wei Ren

This paper studies the inherent connection between dynamic region-following formation control (DRFFC) and distributed average tracking (DAT). We propose a fixed-gain DAT algorithm with robustness to initialization errors for linear multiagent systems, which is capable of achieving DAT with a zero tracking error for a large class of reference signals. In the case that the fixed gain cannot be chosen properly, we present an adaptive control gain design, under which each agent simply chooses its own gain and the restriction on knowing the upper bounds on the reference signals and their inputs is removed. We show that the proposed DAT algorithms can be employed to solve the DRFFC problem. This is an attempt on the applications of DAT algorithms to achieve distributed control; existing works most use DAT as distributed estimation algorithms. For single-integrator, double-integrator, higher-order linear dynamics, we derive the corresponding DRFFC algorithms from the DAT algorithm. Compared with existing DRFFC algorithms, the DAT-based DRFFC algorithms do not require the desired region to have a regular shape and is capable of generating a much richer formation behavior. Numerical examples are also included to show the validity of the derived results.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Hu Xiao; Rongxin Cui; Demin Xu

This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-26
Gyeong-Moon Park; Yong-Ho Yoo; Deok-Hwa Kim; Jong-Hwan Kim

Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-08-17
Dongning Liu; Yunyi Yuan; Haibin Zhu; Shaohua Teng; Changqin Huang

Role assignment is a critical element in the role-based collaboration process. There are many factors to consider when decision makers undertake this task. Such factors include a decision maker’s preferences and the team’s performance. This paper proposes a series of methods, relative to these factors, to solve the group role assignment with balance problem through an association with the one clause at a time approach that is a well-accepted and logic-based association rule mining method. The proposed methods are verified by simulation experiments. The experimental results present the practicability of the proposed solutions. Using the proposed methods, decision makers need only to establish coarse-grain preferences. The fine-grain preferences can be mined. Furthermore, a balance is obtained between the fine-grain preferences and the team’s performance.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-26
Jing Huo; Yang Gao; Yinghuan Shi; Wanqi Yang; Hujun Yin

Heterogeneous face recognition deals with matching face images from different modalities or sources. The main challenge lies in cross-modal differences and variations and the goal is to make cross-modality separation among subjects. A margin-based cross-modality metric learning (MCM2L) method is proposed to address the problem. A cross-modality metric is defined in a common subspace where samples of two different modalities are mapped and measured. The objective is to learn such metrics that satisfy the following two constraints. The first minimizes pairwise, intrapersonal cross-modality distances. The second forces a margin between subject specific intrapersonal and interpersonal cross-modality distances. This is achieved by defining a hinge loss on triplet-based distance constraints for efficient optimization. It allows the proposed method to focus more on optimizing distances of those subjects whose intrapersonal and interpersonal distances are hard to separate. The proposed method is further extended to a kernelized MCM2L (KMCM2L). Both methods have been evaluated on an ID card face dataset and two other cross-modality benchmark datasets. Various feature extraction methods have also been incorporated in the study, including recent deep learned features. In extensive experiments and comparisons with the state-of-the-art methods, the MCM2L and KMCM2L methods achieved marked improvements in most cases.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Sibo Zhang; Licheng Jiao; Fang Liu; Shuang Wang

Low-rank restoration has recently attracted a lot of attention in the research of computer vision. Empirical studies show that exploring the low-rank property of the patch groups can lead to superior restoration performance, however, there is limited achievement on the global low-rank restoration because the rank minimization at image level is too strong for the natural images which seldom match the low-rank condition. In this paper, we describe a flexible global low-rank restoration model which introduces the local statistical properties into the rank minimization. The proposed model can effectively recover the latent global low-rank structure via nuclear norm, as well as the fine details via Gaussian mixture model. An alternating scheme is developed to estimate the Gaussian parameters and the restored image, and it shows excellent convergence and stability. Besides, experiments on image and video sequence datasets show the effectiveness of the proposed method in image inpainting problems.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-26
Fang Wang; Bing Chen; Chong Lin; Jing Zhang; Xinzhu Meng

This paper addresses the finite-time tracking issue for nonlinear quantized systems with unmeasurable states. Compared with the existing researches, the finite-time quantized feedback control is considered for the first time. By proposing a new finite-time stability criterion and designing a state observer, a novel adaptive neural output-feedback control strategy is raised by backstepping technique. Under the presented control scheme, the finite-time quantized feedback control problem is coped with without limiting assumption for nonlinear functions.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Alan Lukežič; Luka Čehovin Zajc; Matej Kristan

Deformable parts models show a great potential in tracking by principally addressing nonrigid object deformations and self occlusions, but according to recent benchmarks, they often lag behind the holistic approaches. The reason is that potentially large number of degrees of freedom have to be estimated for object localization and simplifications of the constellation topology are often assumed to make the inference tractable. We present a new formulation of the constellation model with correlation filters that treats the geometric and visual constraints within a single convex cost function and derive a highly efficient optimization for maximum a posteriori inference of a fully connected constellation. We propose a tracker that models the object at two levels of detail. The coarse level corresponds a root correlation filter and a novel color model for approximate object localization, while the mid-level representation is composed of the new deformable constellation of correlation filters that refine the object location. The resulting tracker is rigorously analyzed on a highly challenging OTB, VOT2014, and VOT2015 benchmarks, exhibits a state-of-the-art performance and runs in real-time.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-07-03
Bo Chen; Daniel W. C. Ho; Guoqiang Hu; Li Yu

State estimation plays an essential role in the monitoring and supervision of cyber-physical systems (CPSs), and its importance has made the security and estimation performance a major concern. In this case, multisensor information fusion estimation (MIFE) provides an attractive alternative to study secure estimation problems because MIFE can potentially improve estimation accuracy and enhance reliability and robustness against attacks. From the perspective of the defender, the secure distributed Kalman fusion estimation problem is investigated in this paper for a class of CPSs under replay attacks, where each local estimate obtained by the sink node is transmitted to a remote fusion center through bandwidth constrained communication channels. A new mathematical model with compensation strategy is proposed to characterize the replay attacks and bandwidth constrains, and then a recursive distributed Kalman fusion estimator (DKFE) is designed in the linear minimum variance sense. According to different communication frameworks, two classes of data compression and compensation algorithms are developed such that the DKFEs can achieve the desired performance. Several attack-dependent and bandwidth-dependent conditions are derived such that the DKFEs are secure under replay attacks. An illustrative example is given to demonstrate the effectiveness of the proposed methods.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-27
Fan Wang; Jinling Liang; Zidong Wang; Xiaohui Liu

This paper is concerned with the recursive filtering problem for a class of nonlinear 2-D time-varying systems with degraded measurements over a finite horizon. The phenomenon of measurement degradation occurs in a random way depicted by stochastic variables satisfying certain probabilities distributions. The nonlinearities under consideration are dealt with through the Taylor expansion, where the high-order terms of the linearization errors are characterized by norm-bounded parameter uncertainties. The objective of the addressed problem is to design a filter which guarantees an upper bound of the estimation error variance and subsequently minimizes such a bound with the desired gain parameters. By means of mathematical induction, an upper bound is first derived for the estimation error variance by constructing two sets of Riccati-like difference equations, and then the obtained bound is minimized by properly selecting the filter parameter at each time step. Both the minimal upper bound and the desired filter parameter are suitable for recursive online computation. Furthermore, the effect of the stochastic measurement degradation on the filtering performance is discussed. Finally, a simulation example is presented to demonstrate the effectiveness of the designed filter.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-07-04
Min Zhao; Chen Peng; Wangli He; Yang Song

This paper is concerned with leader-following consensus of second-order multiagent systems with nonlinear dynamics. First, to save the limited communication resources, a new event-triggered control protocol is delicately developed without requiring continuous communication among the follower agents. Then, by employing the Lyapunov functional method and the Kronecker product technique, a novel sufficient criterion with less conservation is derived to guarantee the leader-following consensus while excluding the Zeno behavior. Furthermore, for the first time, an algorithm to actively adjust the leader adjacency matrix is presented, which efficiently expands the application range of some existing criteria. An example is finally given to illustrate the effectiveness of theoretical results.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-07-06
Yue Li; Devesh K. Jha; Asok Ray; Thomas A. Wettergren

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor’s contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method’s efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-06-30
Chia-Feng Juang; Yen-Ting Yeh

This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimized to generate angles of the hip roll and pitch, the knee pitch, and the ankle pitch and roll. The performance of the FCRNN-generated gait is evaluated according to the walking speed, trajectory straightness, oscillations of the body in the pitch and yaw directions, and walking posture, subject to the basic constraints that the robot cannot fall down and must walk forward. This paper formulates this gait generation task as a constrained multiobjective optimization problem and solves this problem through an AMO-CACO-based evolutionary learning approach. The AMO-CACO finds Pareto optimal solutions through ant-path selection and sampling operations by introducing an accumulated rank for the solutions in each single-objective function into solution sorting to improve learning performance. Simulations are conducted to verify the AMO-CACO-based FCRNN gait generation performance through comparisons with different multiobjective optimization algorithms. Selected software-designed Pareto optimal FCRNNs are then applied to control the gait of a real NAO robot.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-07-04
Xuelong Li; Bin Zhao; Xiaoqiang Lu

Key frame extraction is an efficient way to create the video summary which helps users obtain a quick comprehension of the video content. Generally, the key frames should be representative of the video content, meanwhile, diverse to reduce the redundancy. Based on the assumption that the video data are near a subspace of a high-dimensional space, a new approach, named as key frame extraction in the summary space, is proposed for key frame extraction in this paper. The proposed approach aims to find the representative frames of the video and filter out similar frames from the representative frame set. First of all, the video data are mapped to a high-dimensional space, named as summary space. Then, a new representation is learned for each frame by analyzing the intrinsic structure of the summary space. Specifically, the learned representation can reflect the representativeness of the frame, and is utilized to select representative frames. Next, the perceptual hash algorithm is employed to measure the similarity of representative frames. As a result, the key frame set is obtained after filtering out similar frames from the representative frame set. Finally, the video summary is constructed by assigning the key frames in temporal order. Additionally, the ground truth, created by filtering out similar frames from human-created summaries, is utilized to evaluate the quality of the video summary. Compared with several traditional approaches, the experimental results on 80 videos from two datasets indicate the superior performance of our approach.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2017-07-27
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. The designed distributed protocols guarantee asymptotic output consensus for the agents. Simulation results validate the effectiveness of the proposed composite control scheme.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15
Peiguang Jing; Yuting Su; Xiao Jin; Chengqian Zhang

Time-series prediction has become a prominent challenge, especially when the data are described as sequences of multiway arrays. Because noise and redundancy may exist in the tensor representation of a time series, we focus on solving the problem of high-order time-series prediction under a tensor decomposition framework and develop two novel multilinear models: 1) the multilinear orthogonal autoregressive (MOAR) model and 2) the multilinear constrained autoregressive (MCAR) model. The MOAR model is designed to preserve as much information as possible from the original tensorial data under orthogonal constraints. The MCAR model is an enhanced version that is developed by replacing orthogonal constraints with an inverse decomposition error term. For both models, we project the original tensor into subspaces spanned by basis matrices to facilitate the discovery of the intrinsic temporal structure embedded in the original tensor. To build connections among consecutive slices of the tensor, we generalize a traditional autoregressive model to tensor form to better preserve the temporal smoothness. Experiments conducted on four publicly available datasets demonstrate that our proposed methods converge within a small number of iterations during the training stage and achieve promising results compared with state-of-the-art methods.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15
Jinxing Li; Bob Zhang; Guangming Lu; Hu Ren; David Zhang

Multiview learning methods often achieve improvement compared with single-view-based approaches in many applications. Due to the powerful nonlinear ability and probabilistic perspective of Gaussian process (GP), some GP-based multiview efforts were presented. However, most of these methods make a strong assumption on the kernel function (e.g., radial basis function), which limits the capacity of the real data modeling. In order to address this issue, in this paper, we propose a novel multiview approach by combining a multikernel and GP latent variable model. Instead of designing a deterministic kernel function, multiple kernel functions are established to automatically adapt various types of data. Considering a simple way of obtaining latent variables at the testing stage, a projection from the observed space to the latent space as a back constraint has also been simultaneously introduced into the proposed method. Additionally, different from some existing methods which apply the classifiers off-line, a hinge loss is embedded into the model to jointly learn the classification hyperplane, encouraging the latent variables belonging to the different classes to be separated. An efficient algorithm based on the gradient decent technique is constructed to optimize our method. Finally, we apply the proposed approach to three real-world datasets and the associated results demonstrate the effectiveness and superiority of our model compared with other state-of-the-art methods.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15
Yuan Yuan; Zidong Wang; Peng Zhang; Hongli Dong

In this paper, the near-optimal nonfragile consensus control design problem is investigated for a class of discrete time-varying multiagent systems (MASs) with control- and state-dependent noises. A decentralized observer-based control protocol is proposed by using the relative output measurements. The gain perturbations/variations of the controller as well as the state- and control-dependent noises are simultaneously taken into consideration, which could better reflect the complexities in reality. The corresponding time-varying observer-based nonfragile near-optimal consensus protocol is designed for the underlying MASs over a finite horizon. To be specific, a certain upper bound is first derived for the associate cost function for the MASs. Then, such an upper bound is minimized by using the completing-the-square technique and Moore-Penrose pseudo inverse. The parameters of the time-varying observer/controller are obtained in terms of the solutions to the Riccati-like recursion. In virtue of the matrix partitioning technique, the explicit expressions of the control/observer parameters are presented. Finally, based on the derived consensus protocol, an upper bound of the associate cost function is provided as time goes to infinity. Some numerical simulations are conducted to demonstrate the validity of the proposed methodology.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-15
Li Li; Dongdong Yu; Yuanqing Xia; Hongjiu Yang

This paper concentrates on the remote state estimation problem for nonlinear systems over a communication-limited wireless sensor network. Because of the non-Gaussian property caused by nonlinear transformation, the unscented transformation technique is exploited to obtain approximate Gaussian probability distributions of state and measurement. To reduce excessive data transmission, uncontrollable and controllable stochastic event-triggered scheduling schemes are developed to decide whether the current measurement should be transmitted. Compared with some existing deterministic event-triggered scheduling schemes, the newly developed ones possess a potential superiority in maintaining Gaussian property of innovation process. Under the proposed schemes, two nonlinear state estimators are designed based on the unscented Kalman filter. Stability and convergence conditions of these two estimators are established by analyzing behaviors of estimation error and error covariance. It is shown that an expected compromise between communication rate and estimation quality can be achieved by properly tuning event-triggered parameter matrix. Numerical examples are provided to testify the validity of the proposed results.

更新日期：2018-05-16
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-11
Xiao Sun; Luming Zhang; Zepeng Wang; Jie Chang; Yiyang Yao; Ping Li; Roger Zimmermann

Accurately recognizing sophisticated sceneries from a rich variety of semantic categories is an indispensable component in many intelligent systems, e.g., scene parsing, video surveillance, and autonomous driving. Recently, there have emerged a large quantity of deep architectures for scene categorization, wherein promising performance has been achieved. However, these models cannot explicitly encode human visual perception toward different sceneries, i.e., the sequence of humans sequentially allocates their gazes. To solve this problem, we propose deep gaze shifting kernel to distinguish sceneries from different categories. Specifically, we first project regions from each scenery into the so-called perceptual space, which is established by combining color, texture, and semantic features. Then, a novel non-negative matrix factorization algorithm is developed which decomposes the regions' feature matrix into the product of the basis matrix and the sparse codes. The sparse codes indicate the saliency level of different regions. In this way, the gaze shifting path from each scenery is derived and an aggregation-based convolutional neural network is designed accordingly to learn its deep representation. Finally, the deep representations of gaze shifting paths from all the scene images are incorporated into an image kernel, which is further fed into a kernel SVM for scene categorization. Comprehensive experiments on six scenery data sets have demonstrated the superiority of our method over a series of shallow/deep recognition models. Besides, eye tracking experiments have shown that our predicted gaze shifting paths are 94.6% consistent with the real human gaze allocations.

更新日期：2018-05-12
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-10
Ruimei Zhang; Deqiang Zeng; Ju H. Park; Yajuan Liu; Shouming Zhong

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

更新日期：2018-05-11
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-10
Fan Zheng; Hengbo Tang; Yun-Hui Liu

This paper focuses on the motion estimation problem of ground vehicles using odometry and monocular visual sensors. While the keyframe-based batch optimization methods become the mainstream approach in mobile vehicle localization and mapping, the keyframe poses are usually represented by SE(3) in vision-based methods or SE(2) in methods based on range scanners. For a ground vehicle, this paper proposes a new SE(2)-constrained SE(3) parameterization of its poses, which can be easily achieved in the batch optimization framework using specially formulated edges. Utilizing such a parameterization of poses, a complete odometry-vision-based motion estimation system is developed. The system is designed in a commonly used structure of graph optimization, providing high modularity and flexibility for further implementation or adaptation. Its superior performance in terms of accuracy on a ground vehicle platform is validated by real-world experiments in industrial indoor environments.

更新日期：2018-05-11
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-10
Conor Fahy; Shengxiang Yang; Mario Gongora

A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive online and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an online, bio-inspired approach to clustering dynamic data streams. The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behavior of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.

更新日期：2018-05-11
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-10
Yadi Wang; Xiaoping Li; Rubén Ruiz

Relevant gene selection is crucial for analyzing cancer gene expression datasets including two types of tumors in cancer classification. Intrinsic interactions among selected genes cannot be fully identified by most existing gene selection methods. In this paper, we propose a weighted general group lasso (WGGL) model to select cancer genes in groups. A gene grouping heuristic method is presented based on weighted gene co-expression network analysis. To determine the importance of genes and groups, a method for calculating gene and group weights is presented in terms of joint mutual information. To implement the complex calculation process of WGGL, a gene selection algorithm is developed. Experimental results on both random and three cancer gene expression datasets demonstrate that the proposed model achieves better classification performance than two existing state-of-the-art gene selection methods.

更新日期：2018-05-11
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-08
Chenguang Yang; Guangzhu Peng; Yanan Li; Rongxin Cui; Long Cheng; Zhijun Li

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.

更新日期：2018-05-09
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-04
Jiaojiao Zhang; Shuang Cong; Qing Ling; Kezhi Li

A pure or nearly pure quantum state can be described as a low-rank density matrix, which is a positive semidefinite and unit-trace Hermitian. We consider the problem of recovering such a low-rank density matrix contaminated by sparse components, from a small set of linear measurements. This quantum state estimation task can be formulated as a robust principal component analysis (RPCA) problem subject to positive semidefinite and unit-trace Hermitian constraints. We propose an efficient and fast inexact alternating direction method of multipliers (I-ADMM), in which the subproblems are solved inexactly and hence have closed-form solutions. We prove global convergence of the proposed I-ADMM, and the theoretical result provides a guideline for parameter setting. Numerical experiments show that the proposed I-ADMM can recover state density matrices of 5 qubits on a laptop in 0.69 s, with 6 x 10⁻⁴ accuracy (99.38% fidelity) using 30% compressive sensing measurements, which outperforms existing algorithms.

更新日期：2018-05-05
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-04
Ting Xie; Shutao Li; Leyuan Fang; Licheng Liu

Tensor completion (TC), aiming to recover original high-order data from its degraded observations, has recently drawn much attention in hyperspectral images (HSIs) domain. Generally, the widely used TC methods formulate the rank minimization problem with a convex trace norm penalty, which shrinks all singular values equally, and may generate a much biased solution. Besides, these TC methods assume the whole high-order data is of low-rank, which may fail to recover the detail information in high-order data with diverse and complex structures. In this paper, a novel nonlocal low-rank regularization-based TC (NLRR-TC) method is proposed for HSIs, which includes two main steps. In the first step, an initial completion result is generated by the proposed low-rank regularization-based TC (LRR-TC) model, which combines the logarithm of the determinant with the tensor trace norm. This model can more effectively approximate the tensor rank, since the logarithm function values can be adaptively tuned for each input. In the second step, the nonlocal spatial-spectral similarity is integrated into the LRR-TC model, to obtain the final completion result. Specifically, the initial completion result is first divided into groups of nonlocal similar cubes (each group forms a 3-D tensor), and then the LRR-TC is applied to each group. Since similar cubes within each group contain similar structures, each 3-D tensor should have low-rank property, and thus further improves the completion result. Experimental results demonstrate that the proposed NLRR-TC method outperforms state-of-the-art HSIs completion techniques.

更新日期：2018-05-05
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-04
Lei Liu; Yan-Jun Liu; Shaocheng Tong

This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary. Meanwhile, there exist unknown internal dynamics in the switched system, which are identified by the radial basis function neural networks. It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability. Finally, an illustrative simulation on a resistor-capacitor-inductor circuit is proposed to further demonstrate the effectiveness of the theoretical result.

更新日期：2018-05-05
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-04
Yu Liu; Jingkuan Song; Ke Zhou; Lingyu Yan; Li Liu; Fuhao Zou; Ling Shao

Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets.

更新日期：2018-05-05
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-04
Yu-Long Wang; Qing-Long Han; Min-Rui Fei; Chen Peng

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

更新日期：2018-05-05
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-03
Rahul Raveendran; Biao Huang

In this paper, a hybrid model is proposed to simultaneously mine causal connections and features responsible for contemporaneous correlations in a multivariate process. The model is developed by combining the vector auto-regressive exogenous model and the factor analysis model. The parameters of the resulting model are regularized using the hierarchical prior distributions for pruning insignificant/irrelevant ones from the model. It is then estimated under the variational Bayesian expectation maximization framework. The estimation is initiated with a complex model which is then systematically reduced to a simpler model that retains only the parameters corresponding to significant causal connections and contemporaneous correlations. Model reduction is carried out through a series of deterministic jumps from complex models to simpler models using a relevance criterion. The approach is illustrated with a number of simulated examples and an industrial case study.

更新日期：2018-05-04
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-03
Junyi Yang; Feng Xiao; Jie Ma

This paper investigates the containment control problem of multiagent systems with double integrator dynamics under directed topologies. A model-based edge-event-triggered control protocol is proposed, in which the control input to each agent only contains edge information and its own velocity information. Continuous detection is avoided by the establishment of a predictive model and each controller is only updated at its own event time instants. The theoretical results show that, under our control protocol, the containment control problem can be solved and the Zeno behavior is excluded. The effectiveness is further illustrated by simulation results.

更新日期：2018-05-04
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-02
Kaihong Lu; Gangshan Jing; Long Wang

In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players' actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.

更新日期：2018-05-03
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-02
Shanling Dong; Zheng-Guang Wu; Ya-Jun Pan; Hongye Su; Yang Liu

This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler's lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.

更新日期：2018-05-03
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-02
Xian Yang; Chang-Chun Hua; Jing Yan; Xin-Ping Guan

Most research so far in teleoperation control has assumed that all information is transmitted continuously. Unfortunately, the damaged and electromagnetic interfered line cause communication link failure. In addition, the unreliable link further leads to port data congestion. The data packet will be discarded when the buffer overflows. Consequently, it is unknown whether stability of the teleoperator could be guaranteed in the presence of intermittent communications. In order to overcome these drawbacks, in this paper, we provide a solution to the formation control problem of a single-master-multislave teleoperator in the situation where each robot is allowed to communicate with its neighbors only at some irregular discrete time instants. The relationship among control gains, topology, and maximum-allowable connected interval is presented. Simulations are performed to show the validity of our proposed approach.

更新日期：2018-05-03
• IEEE Trans. Cybern. (IF 7.384) Pub Date : 2018-05-01
Changhe Li; Trung Thanh Nguyen; Sanyou Zeng; Ming Yang; Min Wu

Many artificial benchmark problems have been proposed for different kinds of continuous optimization, e.g., global optimization, multimodal optimization, multiobjective optimization, dynamic optimization, and constrained optimization. However, there is no unified framework for constructing these types of problems and possible properties of many problems are not fully tunable. This will cause difficulties for researchers to analyze strengths and weaknesses of an algorithm. To address these issues, this paper proposes a simple and intuitive framework, which is able to construct different kinds of problems for continuous optimization. The framework utilizes the k-d tree to partition the search space and sets a certain number of simple functions in each subspace. The framework is implemented into global/multimodal optimization, dynamic single objective optimization, multiobjective optimization, and dynamic multiobjective optimization, respectively. Properties of the proposed framework are discussed and verified with traditional evolutionary algorithms.

更新日期：2018-05-01
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.