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  • Integration of Preferences in Decomposition Multiobjective Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Ke Li; Renzhi Chen; Geyong Min; Xin Yao

    Rather than a whole Pareto-optimal front, which demands too many points (especially in a high-dimensional space), the decision maker (DM) may only be interested in a partial region, called the region of interest (ROI). In this case, solutions outside this region can be noisy to the decision-making procedure. Even worse, there is no guarantee that we can find the preferred solutions when tackling problems with complicated properties or many objectives. In this paper, we develop a systematic way to incorporate the DM's preference information into the decomposition-based evolutionary multiobjective optimization methods. Generally speaking, our basic idea is a nonuniform mapping scheme by which the originally evenly distributed reference points on a canonical simplex can be mapped to new positions close to the aspiration-level vector supplied by the DM. By this means, we are able to steer the search process toward the ROI either directly or interactively and also handle many objectives. Meanwhile, solutions lying on the boundary can be approximated as well given the DM's requirements. Furthermore, the extent of the ROI is intuitively understandable and controllable in a closed form. Extensive experiments on a variety of benchmark problems with 2 to 10 objectives, fully demonstrate the effectiveness of our proposed method for approximating the preferred solutions in the ROI.

    更新日期:2018-08-20
  • Pinning Synchronization of Multiplex Delayed Networks With Stochastic Perturbations
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Xueyi Zhao; Jin Zhou; Jun-An Lu

    As the monoplex network has its limitations in describing the real world, a new framework called the multiplex network is put forward and has received much attention in recent years. This paper focuses on synchronization of the multiplex network with multiple delays and stochastic perturbations. Due to the complexity, pinning control of the multiplex network is of particular interest. Based on the LaSalle-type invariance principle for stochastic differential delay equations and the Lyapunov stability theory, some control schemes and synchronization criteria are obtained. It is concluded that under some mild conditions, one can determine which nodes should be pinned in a multiplex network. In addition, it is found that the number of pinned nodes increases with the varying interval of noise and time delay, and decreases with the varying interval of intralayer coupling strength. Some two-layer and three-layer networks are employed to validate the effectiveness of the proposed algorithm.

    更新日期:2018-08-20
  • Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-17
    Lu Dong; Jun Yan; Xin Yuan; Haibo He; Changyin Sun

    This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method.

    更新日期:2018-08-18
  • Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-17
    Rohollah Moghadam; Hamidreza Modares

    An autonomous and resilient controller is proposed for leader-follower multiagent systems under uncertainties and cyber-physical attacks. The leader is assumed nonautonomous with a nonzero control input, which allows changing the team behavior or mission in response to the environmental changes. A resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. An observer-based distributed H∞ controller is first designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as to attenuate the effect of these attacks on the compromised agent itself. Nonhomogeneous game algebraic Riccati equations are derived to solve the H∞ optimal synchronization problem and off-policy reinforcement learning (RL) is utilized to learn their solution without requiring any knowledge of the agent's dynamics. A trust-confidence-based distributed control protocol is then proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based solely on its local evidence. The proposed resilient RL algorithm employs the confidence value of each agent to indicate the trustworthiness of its own information and broadcast it to its neighbors to put weights on the data they receive from it during and after learning. If the confidence value of an agent is low, it employs a trust mechanism to identify compromised agents and remove the data it receives from them from the learning process. The simulation results are provided to show the effectiveness of the proposed approach.

    更新日期:2018-08-18
  • Camera-Assisted Video Saliency Prediction and Its Applications
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-12-21
    Xiao Sun; Yuxing Hu; Luming Zhang; Yanxiang Chen; Ping Li; Zhao Xie; Zhenguang Liu

    Video saliency prediction is an indispensable yet challenging technique which can facilitate various applications, such as video surveillance, autonomous driving, and realistic rendering. Based on the popularity of embedded cameras, we in this paper predict region-level saliency from videos by leveraging human gaze locations recorded using a camera, (e.g., those equipped on an iMAC and laptop PC). Our proposed camera-assisted mechanism improves saliency prediction by discovering human attended regions inside a video clip. It is orthogonal to the current saliency models, i.e., any existing video/image saliency model can be boosted by our mechanism. First of all, the spatial-and temporal-level visual features are exploited collaboratively for calculating an initial saliency map. We notice that the current saliency models are not sufficiently adaptable to the variations in lighting, different view angles, and complicated backgrounds. Therefore, assisted by a camera tracking human gaze movements, a non-negative matrix factorization algorithm is designed to accurately localize the semantically/visually salient video regions perceived by humans. Finally, the learned human gaze locations as well as the initial saliency map are integrated to optimize video saliency calculation. Empirical results thoroughly demonstrated that: 1) our approach achieves the state-of-the-art video saliency prediction accuracy by outperforming 11 mainstream algorithms considerably and 2) our method can conveniently and successfully enhance video retargeting, quality estimation, and summarization.

    更新日期:2018-08-17
  • Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-01-23
    Jun Wan; Zichang Tan; Zhen Lei; Guodong Guo; Stan Z. Li

    Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.

    更新日期:2018-08-17
  • Network-Based T–S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles
    IEEE Trans. Cybern. (IF 8.803) 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:2018-08-17
  • Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-16
    Weiping Ding; Chin-Teng Lin; Witold Pedrycz

    Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.

    更新日期:2018-08-17
  • Robust Landmark Detection and Position Measurement Based on Monocular Vision for Autonomous Aerial Refueling of UAVs
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-16
    Siyang Sun; Yingjie Yin; Xingang Wang; De Xu

    In this paper, a position measurement system, including drogue's landmark detection and position computation for autonomous aerial refueling of unmanned aerial vehicles, is proposed. A multitask parallel deep convolution neural network (MPDCNN) is designed to detect the landmarks of the drogue target. In MPDCNN, two parallel convolution networks are used, and a fusion mechanism is proposed to accomplish the effective fusion of the drogue's two salient parts' landmark detection. Considering the drogue target's geometric constraints, a position measurement method based on monocular vision is proposed. An effective fusion strategy, which fuses the measurement results of drogue's different parts, is proposed to achieve robust position measurement. The error of landmark detection with the proposed method is 3.9%, and it is obviously lower than the errors of other methods. Experimental results on the two KUKA robots platform verify the effectiveness and robustness of the proposed position measurement system for aerial refueling.

    更新日期:2018-08-17
  • Managing Traditional Solar Greenhouse With CPSS: A Just-for-Fit Philosophy
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-16
    Mengzhen Kang; Xing-Rong Fan; Jing Hua; Haoyu Wang; Xiujuan Wang; Fei-Yue Wang

    The profit of greenhouse production is influenced by management activities (e.g., environmental control and plantation scheduling) as well as social conditions (e.g., price fluctuation). In China, the prevailing horticultural facility is the traditional solar greenhouse. The key existing problem is the lack of knowledge of growers, which in turn leads to inefficient management, low production, or unsalable products. To secure effective greenhouse management, the production planning system must account for the crop growing environment, grower's activities, and the market. This paper presents an agricultural cyber-physical-social system (CPSS) serving agricultural production management, with a case study on the solar greenhouse. The system inputs are derived from social and physical sensors, with the former collecting the price of agricultural products in a wholesale market, and the latter collecting the necessary environmental data in the solar greenhouse. Decision support for the cropping plan is provided by the artificial system, computational experiment, and parallel execution-based method, with description intelligence for estimating the crop development and harvest time, prediction intelligence for optimizing the planting time and area according to the expected targets (stable production or maximum gross profit), and prescription intelligence for online system training. The presented system fits the current technical and economic situation of horticulture in China. The application of agricultural CPSS could decrease waste in labor or fertilizer and support sustainable agricultural production.

    更新日期:2018-08-17
  • Distributed Fault Estimation Observer Design With Adjustable Parameters for a Class of Nonlinear Interconnected Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-16
    Ke Zhang; Bin Jiang; Peng Shi

    In this paper, a new distributed fault estimation observer with adjustable parameters is designed for a class of nonlinear interconnected systems. The presented fault estimator consists of proportional and integral terms to improve the accuracy of fault estimation. The observer gain matrices of the proposed fault estimation scheme for the underlying systems are calculated based on robust L₂-L₂ and L₂-L∞ performance. The proposed method achieves a lower performance level in the aspect of quantitative analysis compared with existing fault estimation approaches. A simulation example is provided to demonstrate the effectiveness of the new design method.

    更新日期:2018-08-17
  • Semi-Global Output Consensus for Discrete-Time Switching Networked Systems Subject to Input Saturation and External Disturbances
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-16
    Housheng Su; Yanyan Ye; Yuan Qiu; Yang Cao; Michael Z. Q. Chen

    The semi-global output consensus problem for multiagent systems depicted by discrete-time dynamics subject to external disturbances and input saturation over switching networks is investigated in this paper. Assume that only a small part of subsystems have directly received the output of the exosystem. The distributed consensus algorithms are proposed by adopting the low-gain state feedback and the modified algebraic Riccati equation. Then, the outputs of all subsystems can reach synchronization asymptotically with those of the exosystem by using the proposed consensus protocols on some preconditions. Both the connected switching networks and the jointly connected switching networks are considered for the semi-global output consensus problem, respectively. Some numerical simulation results are shown to validate the theoretical analysis.

    更新日期:2018-08-17
  • Distributed Average Tracking for Lipschitz-Type of Nonlinear Dynamical Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-14
    Yu Zhao; Yongfang Liu; Guanghui Wen; Xinghuo Yu; Guanrong Chen

    In this paper, a distributed average tracking (DAT) problem is studied for Lipschitz-type of nonlinear dynamical systems. The objective is to design DAT algorithms for locally interactive agents to track the average of multiple reference signals. Here, in both dynamics of agents and reference signals, there is a nonlinear term satisfying a Lipschitz-type condition. Three types of DAT algorithms are designed. First, based on state-dependent-gain design principles, a robust DAT algorithm is developed for solving DAT problems without requiring the same initial condition. Second, by using a gain adaption scheme, an adaptive DAT algorithm is designed to remove the requirement that global information, such as the eigenvalue of the Laplacian and the Lipschitz constant, is known to all agents. Third, to reduce chattering and make the algorithms easier to implement, a couple of continuous DAT algorithms based on time-varying or time-invariant boundary layers are designed, respectively, as a continuous approximation of the aforementioned discontinuous DAT algorithms. Finally, some simulation examples are presented to verify the proposed DAT algorithms.

    更新日期:2018-08-17
  • Face Detection With Different Scales Based on Faster R-CNN
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-14
    Wenqi Wu; Yingjie Yin; Xingang Wang; De Xu

    In recent years, the application of deep learning based on deep convolutional neural networks has gained great success in face detection. However, one of the remaining open challenges is the detection of small-scaled faces. The depth of the convolutional network can cause the projected feature map for small faces to be quickly shrunk, and most detection approaches with scale invariant can hardly handle less than 15$x$15 pixel faces. To solve this problem, we propose a different scales face detector (DSFD) based on Faster R-CNN. The new network can improve the precision of face detection while performing as real-time a Faster R-CNN. First, an efficient multitask region proposal network (RPN), combined with boosting face detection, is developed to obtain the human face ROI. Setting the ROI as a constraint, an anchor is inhomogeneously produced on the top feature map by the multitask RPN. A human face proposal is extracted through the anchor combined with facial landmarks. Then, a parallel-type Fast R-CNN network is proposed based on the proposal scale. According to the different percentages they cover on the images, the proposals are assigned to three corresponding Fast R-CNN networks. The three networks are separated through the proposal scales and differ from each other in the weight of feature map concatenation. A variety of strategies is introduced in our face detection network, including multitask learning, feature pyramid, and feature concatenation. Compared to state-of-the-art face detection methods such as UnitBox, HyperFace, FastCNN, the proposed DSFD method achieves promising performance on popular benchmarks including FDDB, AFW, PASCAL faces, and WIDER FACE.

    更新日期:2018-08-17
  • Adaptive Fault-Tolerant Consensus Protocols for Multiagent Systems With Directed Graphs
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-14
    Zhanshan Wang; Yanming Wu; Lei Liu; Huaguang Zhang

    This paper investigates the problem of adaptive fault-tolerant tracking control for the multiagent systems (MASs) under the time-varying actuator faults and bounded unknown control input of the leader. On the basis of the local state information of neighboring agents, an adaptive fault-tolerant control protocol, which consists of the adaptive estimation of faults, is constructed to compensate for the loss of actuator effectiveness in the leader-follower consensus of MASs. Moreover, the modification term in the adaptive estimation can avoid high-frequency oscillations. It is shown that the tracking errors converge to a neighborhood around the origin in the presence of actuator faults, and the performance of the tracking problem is improved. Furthermore, the protocol is distributed in the sense that the coupling gains are independent. Finally, two examples are given to show the effectiveness of the proposed control protocol.

    更新日期:2018-08-17
  • Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-07
    Vignesh Narayanan; Sarangapani Jagannathan

    In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi–Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

    更新日期:2018-08-17
  • Generalized Multi-View Embedding for Visual Recognition and Cross-Modal Retrieval
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-06
    Guanqun Cao; Alexandros Iosifidis; Ke Chen; Moncef Gabbouj

    In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using the Rayleigh quotient, which is extensible for multiple views, supervised learning, and nonlinear embeddings. Numerous methods including canonical correlation analysis, partial least square regression, and linear discriminant analysis are studied using specific intrinsic and penalty graphs within the same framework. Nonlinear extensions based on kernels and (deep) neural networks are derived, achieving better performance than the linear ones. Moreover, a novel multi-view modular discriminant analysis is proposed by taking the view difference into consideration. We demonstrate the effectiveness of the proposed multi-view embedding methods on visual object recognition and cross-modal image retrieval, and obtain superior results in both applications compared to related methods.

    更新日期:2018-08-17
  • Subnormal Distribution Derived From Evolving Networks With Variable Elements
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-10-02
    Minyu Feng; Hong Qu; Zhang Yi; Jürgen Kurths

    During the past decades, power-law distributions have played a significant role in analyzing the topology of scale-free networks. However, in the observation of degree distributions in practical networks and other nonuniform distributions such as the wealth distribution, we discover that, there exists a peak at the beginning of most real distributions, which cannot be accurately described by a monotonic decreasing power-law distribution. To better describe the real distributions, in this paper, we propose a subnormal distribution derived from evolving networks with variable elements and study its statistical properties for the first time. By utilizing this distribution, we can precisely describe those distributions commonly existing in the real world, e.g., distributions of degree in social networks and personal wealth. Additionally, we fit connectivity in evolving networks and the data observed in the real world by the proposed subnormal distribution, resulting in a better performance of fitness.

    更新日期:2018-08-17
  • A Novel Finite-Sum Inequality-Based Method for Robust$H_\infty$Control of Uncertain Discrete-Time Takagi–Sugeno Fuzzy Systems With Interval-Like Time-Varying Delays
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-22
    Xian-Ming Zhang; Qing-Long Han; Xiaohua Ge

    This paper is concerned with the problem of robust${H}_{\infty}$control of an uncertain discrete-time Takagi–Sugeno fuzzy system with an interval-like time-varying delay. A novel finite-sum inequality-based method is proposed to provide a tighter estimation on the forward difference of certain Lyapunov functional, leading to a less conservative result. First, an auxiliary vector function is used to establish two finite-sum inequalities, which can produce tighter bounds for the finite-sum terms appearing in the forward difference of the Lyapunov functional. Second, a matrix-based quadratic convex approach is employed to equivalently convert the original matrix inequality including a quadratic polynomial on the time-varying delay into two boundary matrix inequalities, which delivers a less conservative bounded real lemma (BRL) for the resultant closed-loop system. Third, based on the BRL, a novel sufficient condition on the existence of suitable robust${H}_{\infty}$fuzzy controllers is derived. Finally, two numerical examples and a computer-simulated truck-trailer system are provided to show the effectiveness of the obtained results.

    更新日期:2018-08-17
  • Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-28
    Joanna Turner; Qinggang Meng; Gerald Schaefer; Amanda Whitbrook; Andrea Soltoggio

    This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

    更新日期:2018-08-17
  • Connectivity-Preserving Approach for Distributed Adaptive Synchronized Tracking of Networked Uncertain Nonholonomic Mobile Robots
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-06
    Sung Jin Yoo; Bong Seok Park

    This paper addresses a distributed connectivity-preserving synchronized tracking problem of multiple uncertain nonholonomic mobile robots with limited communication ranges. The information of the time-varying leader robot is assumed to be accessible to only a small fraction of follower robots. The main contribution of this paper is to introduce a new distributed nonlinear error surface for dealing with both the synchronized tracking and the preservation of the initial connectivity patterns among nonholonomic robots. Based on this nonlinear error surface, the recursive design methodology is presented to construct the approximation-based local adaptive tracking scheme at the robot dynamic level. Furthermore, a technical lemma is established to analyze the stability and the connectivity preservation of the total closed-loop control system in the Lyapunov sense. An example is provided to illustrate the effectiveness of the proposed methodology.

    更新日期:2018-08-17
  • Superpixel-Based Foreground Extraction With Fast Adaptive Trimaps
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-12
    Xuelong Li; Kang Liu; Yongsheng Dong

    Extracting the foreground from a given complex image is an important and challenging problem. Although there have been many methods to perform foreground extraction, most of them are time-consuming, and the trimaps used in the matting step are labeled manually. In this paper, we propose a fast interactive foreground extraction method based on the superpixel GrabCut and image matting. Specifically, we first extract superpixels from a given image and apply GrabCut on them to obtain a raw mask. Due to that the resulting mask border is hard and toothing, we further propose fast and adaptive trimaps (FATs), and construct an FATs-basedshared mattingfor computing a refined mask. Finally, by interactive processing, we can obtain the final foreground. Experimental results on theBSDS500andalphamattingdatasets demonstrate that our proposed method is faster than five representative methods, and performs better than the interactive representative methods in terms of the three evaluation criteria: 1) mean square error; 2) sum of absolute difference; and 3) execution time.

    更新日期:2018-08-17
  • Diverse Non-Negative Matrix Factorization for Multiview Data Representation
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-08
    Jing Wang; Feng Tian; Hongchuan Yu; Chang Hong Liu; Kun Zhan; Xiao Wang

    Non-negative matrix factorization (NMF), a method for finding parts-based representation of non-negative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views for comprehensive and accurate data representations. Moreover, real-world datasets often come with high-dimensional features, which demands the efficiency of low-dimensional representation learning approaches. To address these needs, we propose a diverse NMF (DiNMF) approach. It enhances the diversity, reduces the redundancy among multiview representations with a novel defined diversity term and enables the learning process in linear execution time. We further propose a locality preserved DiNMF (LP-DiNMF) for more accurate learning, which ensures diversity from multiple views while preserving the local geometry structure of data in each view. Efficient iterative updating algorithms are derived for both DiNMF and LP-DiNMF, along with proofs of convergence. Experiments on synthetic and real-world datasets have demonstrated the efficiency and accuracy of the proposed methods against the state-of-the-art approaches, proving the advantages of incorporating the proposed diversity term into NMF.

    更新日期:2018-08-17
  • Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-14
    Dong-Juan Li; Da-Peng Li

    In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown dead-zone inputs are nonlinearly embedded into the systems. These properties of the systems will make it very difficult and challenging to construct a stable controller. By introducing a new diffeomorphism coordinate transformation, the controlled system is first transformed into a state-output model. By introducing a group of new variables, an input-output model is finally obtained. Based on the transformed model, the implicit function theorem is used to determine the existence of the ideal controllers and the approximators are employed to approximate the ideal controllers. By using the mean value theorem, the nonaffine functions of systems can become an affine structure but nonaffine terms still exist. The adaptation auxiliary terms are skillfully designed to cancel the effect of the dead-zone input. Based on the Lyapunov difference theorem, the boundedness of all the signals in the closed-loop system can be ensured and the tracking errors are kept in a bounded compact set. The effectiveness of the proposed technique is checked by a simulation study.

    更新日期:2018-08-17
  • Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-12
    Tao Zhou; Fanghui Liu; Harish Bhaskar; Jie Yang

    In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

    更新日期:2018-08-17
  • A Distributed Fuzzy Associative Classifier for Big Data
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-19
    Armando Segatori; Alessio Bechini; Pietro Ducange; Francesco Marcelloni

    Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11 000 000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

    更新日期:2018-08-17
  • Adaptive Neural Network Control for Robotic Manipulators With Unknown Deadzone
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-12-11
    Wei He; Bo Huang; Yiting Dong; Zhijun Li; Chun-Yi Su

    This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle the uncertainty and the unknown deadzone effect, we introduce adaptive neural network (NN) control for robotic manipulators. State-feedback control is introduced first and a high-gain observer is then designed to make the proposed control scheme more practical. One radial basis function NN (RBFNN) is used to tackle the deadzone effect, and the other RBFNN is also proposed to estimate the unknown dynamics of robot. The proposed control is then verified on a two-joint rigid manipulator via numerical simulations and experiments.

    更新日期:2018-08-17
  • Training-Based Gradient LBP Feature Models for Multiresolution Texture Classification
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-18
    Luping Ji; Yan Ren; Guisong Liu; Xiaorong Pu

    Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns. The feature mapping models of gLBP descriptors are constructed by the maximal relative-variation rate (mr2) of rotation-invariant patterns, and then prestored as mapping lookup files. By mapping, initial patterns can be transformed into low-dimensional ones. And then it generates multiresolution texture features via the joint and concatenation of gLBP descriptors on different sampling parameters. A trained nearest neighbor classifier with chi-square distance is applied to classify textures by feature histograms. The experimental results of simulation on five public texture databases show that the proposed method is reliable and efficient in texture classification. In comparison with nine other similar approaches, including two state-of-the-art ones, the proposed method runs faster than most of them and also outperforms all of them in terms of classification accuracy and noise robustness. It achieves higher accuracy and has also better robustness to the Salt&Pepper and Gaussian noise added artificially into texture images.

    更新日期:2018-08-17
  • Learning From Short Text Streams With Topic Drifts
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-18
    Peipei Li; Lu He; Haiyan Wang; Xuegang Hu; Yuhong Zhang; Lei Li; Xindong Wu

    Short text streams such as search snippets and micro blogs have been popular on the Web with the emergence of social media. Unlike traditional normal text streams, these data present the characteristics of short length, weak signal, high volume, high velocity, topic drift, etc. Short text stream classification is hence a very challenging and significant task. However, this challenge has received little attention from the research community. Therefore, a new feature extension approach is proposed for short text stream classification with the help of a large-scale semantic network obtained from a Web corpus. It is built on an incremental ensemble classification model for efficiency. First, more semantic contexts based on the senses of terms in short texts are introduced to make up of the data sparsity using the open semantic network, in which all terms are disambiguated by their semantics to reduce the noise impact. Second, a concept cluster-based topic drifting detection method is proposed to effectively track hidden topic drifts. Finally, extensive studies demonstrate that as compared to several well-known concept drifting detection methods in data stream, our approach can detect topic drifts effectively, and it enables handling short text streams effectively while maintaining the efficiency as compared to several state-of-the-art short text classification approaches.

    更新日期:2018-08-17
  • Reaching Non-Negative Edge Consensus of Networked Dynamical Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-18
    Xiao Ling Wang; Housheng Su; Michael Z. Q. Chen; Xiao Fan Wang; Guanrong Chen

    In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.

    更新日期:2018-08-17
  • A Piecewise-Markovian Lyapunov Approach to Reliable Output Feedback Control for Fuzzy-Affine Systems With Time-Delays and Actuator Faults
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-12-04
    Yanling Wei; Jianbin Qiu; Peng Shi; Ligang Wu

    This paper addresses the problem of delay-dependent robust and reliable$\mathscr {H}_{\infty }$static output feedback (SOF) control for a class of uncertain discrete-time Takagi–Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov–Krasovskii functional, a new$\mathscr {H}_{\infty }$performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.

    更新日期:2018-08-17
  • Robust Stabilization of T–S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-19
    Jinghao Li; Qingling Zhang; Xing-Gang Yan; Sarah K. Spurgeon

    This paper addresses the robust stabilization problem for T–S fuzzy stochastic descriptor systems using an integral sliding mode control paradigm. A classical integral sliding mode control scheme and a nonparallel distributed compensation (Non-PDC) integral sliding mode control scheme are presented. It is shown that two restrictive assumptions previously adopted developing sliding mode controllers for Takagi–Sugeno (T–S) fuzzy stochastic systems are not required with the proposed framework. A unified framework for sliding mode control of T–S fuzzy systems is formulated. The proposed Non-PDC integral sliding mode control scheme encompasses existing schemes when the previously imposed assumptions hold. Stability of the sliding motion is analyzed and the sliding mode controller is parameterized in terms of the solutions of a set of linear matrix inequalities which facilitates design. The methodology is applied to an inverted pendulum model to validate the effectiveness of the results presented.

    更新日期:2018-08-17
  • Event-Triggered Control for the Disturbance Decoupling Problem of Boolean Control Networks
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-06
    Bowen Li; Yang Liu; Kit Ian Kou; Li Yu

    This paper investigates the disturbance decoupling problem (DDP) of Boolean control networks (BCNs) by event-triggered control. Using the semi-tensor product of matrices, algebraic forms of BCNs can be achieved, based on which, event-triggered controllers are designed to solve the DDP of BCNs. In addition, the DDP of Boolean partial control networks is also derived by event-triggered control. Finally, two illustrative examples demonstrate the effectiveness of proposed methods.

    更新日期:2018-08-17
  • Recurrent Neural Network for Kinematic Control of Redundant Manipulators With Periodic Input Disturbance and Physical Constraints
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Yinyan Zhang; Shuai Li; Seifedine Kadry; Bolin Liao

    Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this paper, we propose a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The proposed recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the proposed neural network, the end-effector tracking and regulation errors asymptotically converge to zero in the presence of both input disturbance and the two constraints. Simulation examples and comparisons with an existing controller are also presented to validate the effectiveness and superiority of the proposed controller.

    更新日期:2018-08-13
  • A Non-Monte-Carlo Parameter-Free Learning Automata Scheme Based on Two Categories of Statistics
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Ying Guo; Shenghong Li

    Learning automata (LA), which intellectually explores its optimal state by interacting with an external environment continuously, is encountered widely in artificial intelligence. In the evaluation of LA, it has always been a key issue how to tradeoff between ``accuracy'' and ``speed,'' which substantially touches on parameter tuning. A latest issue in the design of LA methodology involves bearing a parameter-free property, thus removing the tremendous expenses brought by parameter tuning. Nevertheless, the currently existing parameter-free LA schemes generally maintain a Monte-Carlo technique, which helps avoid the tuning process at the cost of more computations. This paper examines a new measurement of parameter-free LA schemes based on statistics which overcome the difficulties found in other counterparts. Specifically, it has innovatively disengaged from the dependance on Monte-Carlo methods. Of greater significance, the learning mechanisms operating in the common stationary environments are likewise extended to the nonstationary environments. Simulations confirm the effectiveness and efficiency of the proposed algorithm, especially its low computation consumption as well as the strong tracking capability to abrupt environmental changes.

    更新日期:2018-08-13
  • Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-10
    Shuang Feng; C. L. Philip Chen

    A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi-Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature nodes of BLS with a group of TS fuzzy subsystems, and the input data are processed by each of them. Instead of aggregating the outputs of fuzzy rules produced by every fuzzy subsystem into one value immediately, all of them are sent to the enhancement layer for further nonlinear transformation to preserve the characteristic of inputs. The defuzzification outputs of all fuzzy subsystem and the outputs of enhancement layer are combined together to obtain the model output. The k-means method is employed to determine the centers of Gaussian membership functions in antecedent part and the number of fuzzy rules. The parameters that need to be calculated in a fuzzy BLS are the weights connecting the outputs of enhancement layer to model output and the randomly initialized coefficients of polynomials in consequent part in fuzzy subsystems, which can be calculated analytically. Therefore, fuzzy BLS retains the fast computational nature of BLS. The proposed fuzzy BLS is evaluated by some popular benchmarks for regression and classification, and compared with some state-of-the-art nonfuzzy and neuro-fuzzy approaches. The results indicate that fuzzy BLS outperforms other models involved. Moreover, fuzzy BLS shows advantages over neuro-fuzzy models regarding to the number of fuzzy rules and training time, which can ease the problem of rule explosion to some extent.

    更新日期:2018-08-11
  • $H∞ Synchronization of Networked Master-Slave Oscillators With Delayed Position Data: The Positive Effects of Network-Induced Delays
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-10
    Yanping Yang; Wangli He; Qing-Long Han; Chen Peng

    This paper is concerned with H∞ synchronization of coupled oscillators in a master-slave framework, in which the oscillators cannot be stabilized by nondelayed sampled position data, but can be stabilized by sampled position data with delays restricted by nonzero lower bounds and upper bounds. A configuration of networked master-slave oscillators with a remote controller is first constructed. Then the positive effects of delays on master-slave synchronization are investigated. Some delay-dependent H∞ synchronization criteria are derived by constructing augmented discretized Lyapunov-Krasovskii functionals for determinate sampling and stochastic sampling, respectively. The controller can be designed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to verify the theoretical results. It is shown that the maximum allowable sampling period in the case of stochastic sampling is larger than the one in the case of determinate sampling. Stochastic sampling can also provide a tradeoff between network-induced delays and the sampling periods, enhancing the master-slave synchronization performance.

    更新日期:2018-08-11
  • Quasi-Synchronization of Delayed Memristive Neural Networks via Region-Partitioning-Dependent Intermittent Control
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-08
    Sanbo Ding; Zhanshan Wang; Huaguang Zhang

    This paper aims at investigating the master-slave quasi-synchronization of delayed memristive neural networks (MNNs) by proposing a region-partitioning-dependent intermittent control. The proposed method is described by three partitions of non-negative real region and an auxiliary positive definite function. Whether the control input is imposed on the slave system or not is decided by the dynamical relationships among the three subregions and the auxiliary function. From these ingredients, several succinct criteria with the associated co-design procedure are presented such that the synchronization error converges to a predetermined level. The proposed intermittent control scheme is also applied to the event-triggered control, and an intermittent event-triggered mechanism is devised to investigate the quasi-synchronization of MNNs correspondingly. Such mechanism eliminates the events in rest time, and then it reduces the amount of samplings. Finally, two illustrative examples are presented to verify the effectiveness of our theoretical results.

    更新日期:2018-08-10
  • A Kinematic Model for Swarm Finite-Time Trajectory Tracking
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-07
    Giuseppe Fedele; Luigi D'Alfonso

    This paper focuses on the trajectory tracking problem for a swarm of mobile agents. A kinematic model describing the interactions and evolutions of the swarm members is proposed and its main properties are analyzed emphasizing that the agents centroid is ensured to track in finite-time a given reference trajectory and that the agents reach an aggregation in finite-time in a hyper-ball moving around the centroid path. One of the main characteristics of the model is the presence of an interaction matrix, between agents coordinates, which allows to define some properties of the swarm allowing the creation of different forms of agents aggregations, i.e., spheres, ellipsoids, straight lines, etc. Indeed swarm properties related to the agents configuration around the performed path along with agents interactions and absence of collisions are analyzed depending on the chosen interaction matrix.

    更新日期:2018-08-08
  • Neuroadaptive Robotic Control Under Time-Varying Asymmetric Motion Constraints: A Feasibility-Condition-Free Approach
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-03
    Kai Zhao; Yongduan Song

    This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.

    更新日期:2018-08-06
  • Fault Estimation and Accommodation of Interconnected Systems: A Separation Principle
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Hao Yang; Chengkai Huang; Bin Jiang; Marios M. Polycarpou

    This paper addresses the fault estimation (FE) and accommodation issues of interconnected systems by using two new concepts namely interconnected separation principle and constrained interconnected separation principle that allow for the separate design not only between diagnostic observer and fault tolerant controller for each subsystem, but also between observer/controller of each subsystem and those of other ones. Sufficient fault recoverability conditions are established, under which both distributed and decentralized FE and accommodation schemes are provided. The new results help to provide a framework for observer-based fault diagnosis and fault tolerant control of interconnected systems, and are further applied to the meta aircraft configuration that consists of multiple aircraft joined together to illustrate their efficiency.

    更新日期:2018-08-06
  • Distributed Optimization of Multiagent Systems With Preserved Network Connectivity
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-02
    Boda Ning; Qing-Long Han; Zongyu Zuo

    This paper deals with the problem of distributed optimization of a multiagent system with network connectivity preservation. In order to realize cooperative interactions, a connected network is the prerequisite for high-quality information exchange among agents. However, sensing or communication capability is range-limited, so it is impractical to simply make an assumption that network connectivity is preserved by default. To address this concern, a class of generalized potentials including discontinuities caused by unexpected obstacles or noises are designed. For a class of quadratic cost functions, based on the potentials, a new distributed protocol is proposed to formally guarantee the network connectivity over time and to realize the state agreement in finite time while the sum of local functions known to individual agents is optimized. Since the right-hand side of the proposed protocol is discontinuous, some nonsmooth analysis tools are applied to analyze system performance. In some practical scenarios, where initial states are unavailable, a distributed protocol is further developed to realize the consensus in a prescribed finite time while solving the distributed optimization problem and maintaining network connectivity. Illustrative examples are provided to demonstrate the effectiveness of the proposed protocols.

    更新日期:2018-08-03
  • A Survey of Cognitive Architectures in the Past 20 Years
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-02
    Peijun Ye; Tao Wang; Fei-Yue Wang

    Building autonomous systems that achieve human level intelligence is one of the primary objectives in artificial intelligence (AI). It requires the study of a wide range of functions robustly across different phases of human cognition. This paper presents a review of agent cognitive architectures in the past 20 year's AI research. Different from software structures and simulation environments, most of the architectures concerned are established from mathematics and philosophy. They are categorized according to their knowledge processing patterns--symbolic, emergent or hybrid. All the relevant literature can be accessed publicly, particularly through the Internet. Available websites are also summarized for further reference.

    更新日期:2018-08-02
  • Kinematic Characterization of a Target-Defense Problem With an Interception and Expelling Strategy
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-02
    Jiabao Zhao; Wei Li

    This paper considers a target-defense problem of one defender and one adversary (or called intruder), in which the defender tries to approach a desired interception-position between the intruder and the target to intercept the intruder and expel it from the target. The defender adopts this interception and expelling strategy since the defender is assumed to not capture or destroy the intruder. An expelling-decay exponent is introduced to characterize the expelling-decay rate on the intruder. The system is nonlinear, with distinct physical meaning and rich kinematics. The main concern in this paper is the analysis of the kinematic properties. First, two motion patterns of the system are characterized with respect to different values of the system parameters. Then, the stability and the transition condition of the two motion patterns are provided. Finally, the optimal interception of the defender is provided, which interestingly coincides with the transition condition for the two motion patterns. The interpretations for the physical meaning of the optimal interception are also provided.

    更新日期:2018-08-02
  • Learning-Based Adaptive Attitude Control of Spacecraft Formation With Guaranteed Prescribed Performance
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-02
    Caisheng Wei; Jianjun Luo; Honghua Dai; Guangren Duan

    This paper investigates a novel leader-following attitude control approach for spacecraft formation under the preassigned two-layer performance with consideration of unknown inertial parameters, external disturbance torque, and unmodeled uncertainty. First, two-layer prescribed performance is preselected for both the attitude angular and angular velocity tracking errors. Subsequently, a distributed two-layer performance controller is devised, which can guarantee that all the involved closed-loop signals are uniformly ultimately bounded. In order to tackle the defect of statically two-layer performance controller, learning-based control strategy is introduced to serve as an adaptive supplementary controller based on adaptive dynamic programming technique. This enhances the adaptiveness of the statically two-layer performance controller with respect to unexpected uncertainty dramatically, without any prior knowledge of the inertial information. Furthermore, by employing the robustly positively invariant theory, the input-to-state stability is rigorously proven under the designed learning-based distributed controller. Finally, two groups of simulation examples are organized to validate the feasibility and effectiveness of the proposed distributed control approach.

    更新日期:2018-08-02
  • Multilateral Teleoperation With New Cooperative Structure Based on Reconfigurable Robots and Type-2 Fuzzy Logic
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-02
    Da Sun; Qianfang Liao; Xiaoyi Gu; Changsheng Li; Hongliang Ren

    This paper develops an innovative multilateral teleoperation system with two haptic devices on the master side and a newly designed reconfigurable multi-fingered robot on the slave side. A novel nonsingular fast terminal sliding-mode algorithm, together with varying dominance factors for cooperation, is proposed to offer this system's fast position and force tracking, as well as an integrated perception for the operator on the reconfigurable slave robot (manipulator). The Type-2 fuzzy model is used to describe the overall system dynamics, and accordingly a new fuzzy-model-based state observer is proposed to compensate for system uncertainties. A sliding-mode adaptive controller is designed to deal with the varying zero drift of the force sensors and force observers. The stability of the closed-loop system under time-varying delays is proved using Lyapunov-Krasovskii functions. Finally, experiments to grasp different objects are performed to verify the effectiveness of this multilateral teleoperation system.

    更新日期:2018-08-02
  • Prioritizing Useful Experience Replay for Heuristic Dynamic Programming-Based Learning Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-31
    Zhen Ni; Naresh Malla; Xiangnan Zhong

    The adaptive dynamic programming controller usually needs a long training period because the data usage efficiency is relatively low by discarding the samples once used. Prioritized experience replay (ER) promotes important experiences and is more efficient in learning the control process. This paper proposes integrating an efficient learning capability of prioritized ER design into heuristic dynamic programming (HDP). First, a one time-step backward state-action pair is used to design the ER tuple and, thus, avoids the model network. Second, a systematic approach is proposed to integrate the ER into both critic and action networks of HDP controller design. The proposed approach is tested for two case studies: a cart-pole balancing task and a triple-link pendulum balancing task. For fair comparison, we set the same initial weight parameters and initial starting states for both traditional HDP and the proposed approach under the same simulation environment. The proposed approach improves the required average number of trials to succeed by 60.56% for cart-pole, and 56.89% for triple-link balancing tasks, in comparison with the traditional HDP approach. Also, we have added results of ER-based HDP for comparison. Moreover, theoretical convergence analysis is presented to guarantee the stability of the proposed control design.

    更新日期:2018-08-01
  • A Novel Neurodynamic Approach to Constrained Complex-Variable Pseudoconvex Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-31
    Na Liu; Sitian Qin

    Complex-variable pseudoconvex optimization has been widely used in numerous scientific and engineering optimization problems. A neurodynamic approach is proposed in this paper for complex-variable pseudoconvex optimization problems subject to bound and linear equality constraints. An efficient penalty function is introduced to guarantee the boundedness of the state of the presented neural network, and make the state enter the feasible region of the considered optimization in finite time and stay there thereafter. The state is also shown to be convergent to an optimal point of the considered optimization. Compared with other neurodynamic approaches, the presented neural network does not need any penalty parameters, and has lower model complexity. Furthermore, some additional assumptions in other existing related neural networks are also removed in this paper, such as the assumption that the objective function is lower bounded over the equality constraint set and so on. Finally, some numerical examples and an application in beamforming formulation are provided.

    更新日期:2018-08-01
  • An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-31
    Hang Xu; Wenhua Zeng; Xiangxiang Zeng; Gary G. Yen

    The existing multiobjective evolutionary algorithms (EAs) based on nondominated sorting may encounter serious difficulties in tackling many-objective optimization problems (MaOPs), because the number of nondominated solutions increases exponentially with the number of objectives, leading to a severe loss of selection pressure. To address this problem, some existing many-objective EAs (MaOEAs) adopt Euclidean or Manhattan distance to estimate the convergence of each solution during the environmental selection process. Nevertheless, either Euclidean or Manhattan distance is a special case of Minkowski distance with the order P=2 or P=1, respectively. Thus, it is natural to adopt Minkowski distance for convergence estimation, in order to cover various types of Pareto fronts (PFs) with different concavity-convexity degrees. In this paper, a Minkowski distance-based EA is proposed to solve MaOPs. In the proposed algorithm, first, the concavity-convexity degree of the approximate PF, denoted by the value of P, is dynamically estimated. Subsequently, the Minkowski distance of order P is used to estimate the convergence of each solution. Finally, the optimal solutions are selected by a comprehensive method, based on both convergence and diversity. In the experiments, the proposed algorithm is compared with five state-of-the-art MaOEAs on some widely used benchmark problems. Moreover, the modified versions for two compared algorithms, integrated with the proposed P-estimation method and the Minkowski distance, are also designed and analyzed. Empirical results show that the proposed algorithm is very competitive against other MaOEAs for solving MaOPs, and two modified compared algorithms are generally more effective than their predecessors.

    更新日期:2018-08-01
  • Flexibility Degree of Fuzzy Numbers and Its Implication to a Group-Decision-Making Model
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-27
    Fang Liu; Witold Pedrycz; Xin-Wang Liu

    The theory of fuzzy sets considers that everything exhibits some elasticity and is a matter of degree. When fuzzy numbers are used to evaluate the judgements of decision makers (DMs) in pairwise comparisons of alternatives following the analytic hierarchy process, the flexibility experienced by DMs has been exhibited. In order to capture this aspect of flexibility, it is important to know how to realize the flexibility degree of fuzzy numbers and further present a method of realizing its quantification. In this paper, a definition of the flexibility degree of fuzzy numbers is proposed. Some formulas are proposed to quantify the flexibility and rigidity degrees of interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers. A group decision making (GDM) model is developed under the consideration of the flexibility of DMs. By considering the effects of the applied scale and the reciprocal relation, the flexibility degree of interval multiplicative reciprocal comparison matrices is further defined, which is used to evaluate the flexibility degree of the DM involved in the decision process. An RD-IOWGA operator is proposed to aggregate individual interval multiplicative reciprocal matrices by associating more importance to that with less flexibility. A new algorithm is shown to solve GDM problems with interval multiplicative reciprocal preference relations. Numerical studies are carried out to illustrate the new definitions and offer some comparative analysis. The observations reveal that the developed consensus method can be used to model the GDM with a dominant position.

    更新日期:2018-07-28
  • DIOD: Fast and Efficient Weakly Semi-Supervised Deep Complex ISAR Object Detection
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-27
    Bin Xue; Ningning Tong

    Inverse synthetic aperture radar (ISAR) object detection is one of the most important and challenging problems in computer vision tasks. To provide a convenient and high-quality ISAR object detection method, a fast and efficient weakly semi-supervised method, called deep ISAR object detection (DIOD), is proposed, based on advanced region proposal networks (ARPNs) and weakly semi-supervised deep joint sparse learning: 1) to generate high-level region proposals and localize potential ISAR objects robustly and accurately in minimal time, ARPN is proposed based on a multiscale fully convolutional region proposal network and a region proposal classification and ranking strategy. ARPN shares common convolutional layers with the Inception-ResNet-based system and offers almost cost-free proposal computation with excellent performance; 2) to solve the difficult problem of the lack of sufficient annotated training data, especially in the ISAR field, a convenient and efficient weakly semi-supervised training method is proposed with the weakly annotated and unannotated ISAR images. Particularly, a pairwise-ranking loss handles the weakly annotated images, while a triplet-ranking loss is employed to harness the unannotated images; and 3) to further improve the accuracy and speed of the whole system, a novel sharable-individual mechanism and a relational-regularized joint sparse learning strategy are introduced to achieve more discriminative and comprehensive representations while learning the shared- and individual-features and their correlations. Extensive experiments are performed on two real-world ISAR datasets, showing that DIOD outperforms existing state-of-the-art methods and achieves higher accuracy with shorter execution time.

    更新日期:2018-07-28
  • Design of Highly Nonlinear Substitution Boxes Based on I-Ching Operators
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Tong Zhang; C. L. Philip Chen; Long Chen; Xiangmin Xu; Bin Hu

    This paper is to design substitution boxes (S-Boxes) using innovative I-Ching operators (ICOs) that have evolved from ancient Chinese I-Ching philosophy. These three operators- intrication, turnover, and mutual- inherited from I-Ching are specifically designed to generate S-Boxes in cryptography. In order to analyze these three operators, identity, compositionality, and periodicity measures are developed. All three operators are only applied to change the output positions of Boolean functions. Therefore, the bijection property of S-Box is satisfied automatically. It means that our approach can avoid singular values, which is very important to generate S-Boxes. Based on the periodicity property of the ICOs, a new network is constructed, thus to be applied in the algorithm for designing S-Boxes. To examine the efficiency of our proposed approach, some commonly used criteria are adopted, such as nonlinearity, strict avalanche criterion, differential approximation probability, and linear approximation probability. The comparison results show that S-Boxes designed by applying ICOs have a higher security and better performance compared with other schemes. Furthermore, the proposed approach can also be used to other practice problems in a similar way.

    更新日期:2018-07-24
  • Finite-Time Passivity and Synchronization of Coupled Reaction-Diffusion Neural Networks With Multiple Weights
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Jin-Liang Wang; Xiao-Xiao Zhang; Huai-Ning Wu; Tingwen Huang; Qing Wang

    In this paper, two multiple weighted coupled reaction-diffusion neural networks (CRDNNs) with and without coupling delays are introduced. On the one hand, some finite-time passivity (FTP) concepts are proposed for the spatially and temporally system with different dimensions of output and input. By choosing appropriate Lyapunov functionals and controllers, several sufficient conditions are presented to ensure the FTP of these CRDNNs. On the other hand, the finite-time synchronization (FTS) problem is also discussed for the multiple weighted CRDNNs with and without coupling delays, respectively. Finally, two numeral examples with simulation results are provided to verify the effectiveness of the obtained FTP and FTS criteria.

    更新日期:2018-07-24
  • Distributed LQR Optimal Protocol for Leader-Following Consensus
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Hui Sun; Yungang Liu; Fengzhong Li; Xinglong Niu

    This paper addresses the linear quadratic regulator optimal leader-following consensus for multiagent systems in a single-integrator form. Substantially different from the existing related works, the cost function, a global one, and the topology structure are both pregiven, and the optimal protocol to be sought is distributed (which merely depends on relative state information). This violates the optimal protocol design based on the algebraic Riccati equation, although a centralized protocol can be derived. To solve the problem, a novel design strategy of distributed optimal protocol is proposed for the multiagent systems over the digraph of a directed tree. Specifically, the dynamics of the consensus error is explicitly obtained, by which an online-implementable algorithm is given to achieve the parameterization of the cost function. Namely, the completely explicit formula with respect to the gain parameters of all agents is derived for the cost function. Based on this, the existence of optimal gain parameters is rigorously proven, which means the existence of the desired distributed optimal protocol. Furthermore, the optimal gain parameters are derived by minimizing the explicit formula. Two simulation examples are provided to illustrate the effectiveness of the theoretical results.

    更新日期:2018-07-24
  • Design of a K-Winners-Take-All Model With a Binary Spike Train
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Pavlo V. Tymoshchuk; Donald C. Wunsch

    A continuous-time K-winners-take-all (KWTA) neural model that can identify the largest K of N inputs, where command signal 1 łe K < N is described. The model is given by a differential equation where the spike train is a sum of delta functions. A functional block-diagram of the model includes N feed-forward hard-limiting neurons and one feedback neuron, used to handle input dynamics. The existence and uniqueness of the model steady states are analyzed, the convergence analysis of the state variable trajectories to the KWTA operation is proven, the convergence time and number of spikes required are derived, as well as the processing of time-varying inputs and perturbations of the model nonlinearities are analyzed. The main advantage of the model is that it is not subject to the intrinsic convergence of speed limitations of comparable designs. The model also has an arbitrary finite resolution determined by a given parameter, low complexity, and initial condition independence. Applications of the model for parallel sorting and parallel rank-order filtering are presented. Theoretical results are derived and illustrated with computer-simulated examples that demonstrate the model's performance.

    更新日期:2018-07-24
  • A Grid Weighted Sum Pareto Local Search for Combinatorial Multi and Many-Objective Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Xinye Cai; Haoran Sun; Qingfu Zhang; Yuhua Huang

    Combinatorial multiobjective optimization problems (CMOPs) are very popular due to their widespread applications in the real world. One common method for CMOPs is Pareto local search (PLS), a natural extension of single-objective local search (LS). However, classical PLS tends to reserve all of the nondominated solutions for LS, which causes the inefficient LS, as well as unbearable computational and space cost. Due to the aforementioned reasons, most PLS approaches can only handle CMOPs with no more than two objectives. In this paper, by combining the Pareto dominance and weighted sum (WS) approach in a grid system, the grid weighted sum dominance (gws-dominance) is proposed and integrated into PLS for CMOPs with multiple objectives. In the grid system, at most one representative solution is maintained in each grid for more efficient LS, thus largely reducing the computational and space complexity. The grid-based WS approach can further guide the LS in different grids for maintaining more widely and uniformly distributed Pareto front approximations. In the experimental studies, the grid WS PLS is compared with the classical PLS, three decomposition-based LS approaches [multiobjective evolutionary algorithm based on decomposition-LS (WS, Tchebycheff, and penalty-based boundary intersection)], a grid-based algorithm (ε-MOEA), and a state-of-the-art hybrid approach (multiobjective memetic algorithm based on decomposition) on two sets of benchmark CMOPs. The experimental results show that the grid weighted sum Pareto local search significantly outperforms the compared algorithms and remains effective and efficient on combinatorial multiobjective and even many-objective optimization problems.

    更新日期:2018-07-24
  • Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Yang Cong; Dongying Tian; Yun Feng; Baojie Fan; Haibin Yu

    Realtime 3-D object detection and 6-DOF pose estimation in clutter background is crucial for intelligent manufacturing, for example, robot feeding and assembly, where robustness and efficiency are the two most desirable goals. Especially for various metal parts with a textless surface, it is hard for most state of the arts to extract robust feature from the clutter background with various occlusions. To overcome this, in this paper, we propose an online 3-D object detection and pose estimation method to overcome self-occlusion for textureless objects. For feature representation, we only adopt the raw 3-D point clouds with normal cues to define our local reference frame and we automatically learn the compact 3-D feature from the simple local normal statistics via autoencoder. For a similarity search, a new basis buffer k-d tree method is designed without suffering branch divergence; therefore, ours can maximize the GPU parallel processing capabilities especially in practice. We then generate the hypothesis candidates via the hough voting, filter the false hypotheses, and refine the pose estimation via the iterative closest point strategy. For the experiments, we build a new 3-D dataset including industrial objects with heavy self-occlusions and conduct various comparisons with the state of the arts to justify the effectiveness and efficiency of our method.

    更新日期:2018-07-24
  • Semisupervised Regression With Optimized Rank for Matrix Data Classification
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Jianguang Zhang; Jianmin Jiang; Yahong Han

    There has been growing interest in developing more effective algorithms for matrix data classification. At present, most of the existing vector-based classifications involve vectorization process, which results in two main problems. First, the underlying structural information is disregarded. Second, the vectorization of a matrix incurs the creation of a vector with potentially very high dimensionality, which may lead to over-fitting when the number of training data is small. To avoid such problems, we propose a new matrix-based regression algorithm for classification, in which the input matrices to be classified are directly used to learn two regression matrices for each order of the input matrix. To further explore the discrimination information, we add a joint ℓ2,1-norm on two regression matrices, which endows the algorithm optimized regression rank by uncovering common sparse columns in the two regression matrices. To further boost the classification performance, we incorporate a semisupervised learning process, which leverages both labeled and unlabeled data to enhance the training process. Experiments on public benchmark datasets show that our method outperforms a number of the existing state-of-the-art classification methods even when only few labeled training samples are provided.

    更新日期:2018-07-24
  • A Pareto-Based Sparse Subspace Learning Framework
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-23
    Juanjuan Luo; Licheng Jiao; Fang Liu; Shuyuan Yang; Wenping Ma

    High-dimensionality is a common characteristic of real-world data, which often results in high time and space complexity or poor performance of ensuing methods. Subspace learning, as one kind of dimension reduction method, provides a way to overcome the aforementioned problem. In this paper, we introduce multiobjective evolutionary optimization into subspace learning, and propose a Pareto-based sparse subspace learning algorithm for classification tasks. The proposed algorithm aims at minimizing two conflicting objective functions, the reconstruction error and the sparsity. A kernel trick derived from Gaussian kernel is implemented to the sparse subspace learning for the nonlinear phenomena of nature. In order to speed up the convergence, an entropy-driven initialization scheme and a gradient-descent mutation scheme are designed specifically. At last, a knee point is selected from the Pareto front to guarantee that we can obtain a solution with good classification performance, and yet as sparse as possible. The experiments and detailed analysis on real-life datasets and the hyperspectral images demonstrated that the proposed model achieves comparable results with the existing conventional subspace learning and evolutionary feature selection algorithms. Hence, this paper provides a more flexible and efficient approach for sparse subspace learning.

    更新日期:2018-07-24
  • Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-08-02
    Zaidao Wen; Biao Hou; Qian Wu; Licheng Jiao

    This paper develops a novel iterative framework for subspace clustering (SC) in a learned discriminative feature domain. This framework consists of two modules of fuzzy sparse SC and discriminative transformation learning. In the first module, fuzzy latent labels containing discriminative information and latent representations capturing the subspace structure will be simultaneously evaluated in a feature domain. Then the linear transforming operator with respect to the feature domain will be successively updated in the second module with the advantages of more discrimination, subspace structure preservation, and robustness to outliers. These two modules will be alternatively carried out and both theoretical analysis and empirical evaluations will demonstrate its effectiveness and superiorities. In particular, experimental results on three benchmark databases for SC clearly illustrate that the proposed framework can achieve significant improvements than other state-of-the-art approaches in terms of clustering accuracy.

    更新日期:2018-07-18
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.
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