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  • Guest Editorial From Intelligent Control to Smart Management of Cyber-Physical-Social Systems: A Celebration of 70th Anniversary of Cybernetics by Norbert Wiener
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-15
    Fei-Yue Wang; Dimitar P. Filev; Witold Pedrycz; Hongyi Li; Chelsea C. White

    Inspired by the idealism embodied in Russell and Whitehead’s “Principia Mathematica,” Wiener marched along a different and unique path toward sciences of intelligence and behavior which culminated at “Cybernetics: Or Control and Communication in the Animal and the Machine” 70 years ago. Since then, we have witnessed the birth of Cognitive Science, Artificial Intelligence (AI), Computational Intelligence, and many other new research fields and disciplines, all of which have been catalyzed by Cybernetics. The IEEE Systems, Man, AND Cybernetics Society and this Transactions on Cybernetics have become the focal point of the broad cybernetics community by promoting the theory, practice, and interdisciplinary aspects of systems science and engineering, human-machine systems, and cybernetics principles. It is a time of celebration and reflection.

    更新日期:2018-11-16
  • 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:2018-11-16
  • Parallel Control of Distributed Parameter Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-26
    Yuhua Song; Xiuyu He; Zhijie Liu; Wei He; Changyin Sun; Fei-Yue Wang

    In this paper, we study the control problems of distributed parameter systems, and discuss the limitations of traditional control methods. In recent years, social factors have gradually become an essential parameter of system modeling. For complex distributed parameter systems, the accurate modeling becomes difficult. With the rapid development of the network and the technology of big data and cloud computing, based on the advanced control theory of large-scale computing, we introduce the idea of parallel control to the control of distributed parameter systems. Parallel control is a method to accomplish tasks through the interaction of virtual and actual. Its core is to model the complex distributed parameter system on artificial society or artificial system, then analyze and evaluate it by computational experiment, and finally control and manage the distributed parameter system by parallel execution. Data-driven control and computational control are used in this method, which is a control idea that adapts to the rapid development of society.

    更新日期:2018-11-16
  • Optimal Data Injection Attacks in Cyber-Physical Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-06-26
    Guangyu Wu; Jian Sun; Jie Chen

    The primary goal of this paper is to analyze the dynamic response of a system under optimal data injection attacks from a control perspective. In this paper, optimal data injection attack design problems are formulated in a similar framework of optimal control. We consider a scenario, where an attacker injects false data to a healthy plant comprising many actuators distributed in different regions. For the case, where an attacker pollutes all actuators, an optimal state feedback injection law is proposed to minimize a quadratic cost functional containing two conflicting objectives. For the case, where the attacker only pollutes partial actuators within a short period, the quadratic programming is employed to solve an optimal switching data injection attack design problem using the technique of embedded transformation. A bang-bang-type solution of the quadratic programming exists on account of the minimum value of the Hamilton functional and is achieved at an extreme point of the convex set. Consequently, a switching condition is derived to obtain the optimal attack sequence. We also introduce a closed-form switching policy for data injection attacks with multiple objectives, which is shown optimal in the sense of minimizing a hybrid quadratic performance criterion. Finally, applications of our approaches to a networked dc motor and a power system are provided to illustrate the effectiveness of the proposed method.

    更新日期:2018-11-16
  • Coordinated Optimization for the Descent Gradient of Technical Index in the Iron Removal Process
    IEEE Trans. Cybern. (IF 8.803) 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:2018-11-16
  • Noncooperative Game Strategy in Cyber-Financial Systems With Wiener and Poisson Random Fluctuations: LMIs-Constrained MOEA Approach
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-17
    Bor-Sen Chen; Wei-Yu Chen; Chun-Tao Young; Zhiguo Yan

    The financial market is a nonlinear stochastic system with continuous Wiener and discontinuous Poisson random fluctuations. Most managers or investors hope their investment policies to be with the not only high profit but also low risk. Managers and investors involved pursue their own interests which are partly conflicting with others. Stochastic game theory has been widely applied to multiperson noncooperative decision making problem of financial market. However, for the nonlinear stochastic financial system with random fluctuations, it still lacks an analytical or computational scheme to effectively solve the complex noncooperative game strategy design problem. In this paper, the stochastic multiperson noncooperative game strategy in cyber-financial systems is transformed to a multituple Hamilton–Jacobi–Isacc inequalities (HJIIs)-constrained multiobjective optimization problem (MOP). This HJIIs-constrained MOP solution is also found to be the Nash equilibrium solution of multiperson noncooperative game strategy in nonlinear stochastic financial systems. In order to simplify design procedure by the global linearization theory, a set of local linear systems are interpolated to approximate the nonlinear stochastic financial system so that the m-tuple HJIIs-constrained MOP for noncooperative game strategy of cyber-financial system could be converted to a linear matrix inequalities (LMIs)-constrained MOP. Finally, an LMIs-constrained multiobjective evolution algorithm is explored for effectively solving the multiperson noncooperative game strategy in cyber-financial systems. Two design examples are also given for the illustration of the design procedure and the performance validation of the proposed stochastic noncooperative investment strategy in the nonlinear stochastic financial systems.

    更新日期:2018-11-16
  • Adaptive$Q$-Learning for Data-Based Optimal Output Regulation With Experience Replay
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-04-27
    Biao Luo; Yin Yang; Derong Liu

    In this paper, the data-based optimal output regulation problem of discrete-time systems is investigated. An off-policy adaptive${Q}$-learning (QL) method is developed by using real system data without requiring the knowledge of system dynamics and the mathematical model of utility function. By introducing the${Q}$-function, an off-policy adaptive QL algorithm is developed to learn the optimal${Q}$-function. An adaptive parameter${\alpha _{i}}$in the policy evaluation is used to achieve tradeoff between the current and future${Q}$-functions. The convergence of adaptive QL algorithm is proved and the influence of the adaptive parameter is analyzed. To realize the adaptive QL algorithm with real system data, the actor-critic neural network (NN) structure is developed. The least-squares scheme and the batch gradient descent method are developed to update the critic and actor NN weights, respectively. The experience replay technique is employed in the learning process, which leads to simple and convenient implementation of the adaptive QL method. Finally, the effectiveness of the developed adaptive QL method is verified through numerical simulations.

    更新日期:2018-11-16
  • 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:2018-11-16
  • Integration of Preferences in Decomposition Multiobjective Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-08-20
    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:2018-11-16
  • 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:2018-11-16
  • Parallel Intelligent Systems for Integrated High-Speed Railway Operation Control and Dynamic Scheduling
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-17
    Hairong Dong; Hainan Zhu; Yidong Li; Yisheng Lv; Shigen Gao; Qi Zhang; Bin Ning

    The information exchange gap between current operation control and dynamic scheduling in high-speed railway systems (HRSs) still exists, and this gap has hindered the further integrative improvement of HRSs. This paper aims to explore a feasible solution to bridging the information exchange gap for further improving the efficiency of HRSs, with the parallel intelligent systems for integrated HRS operation control and dynamic scheduling first analyzed and constructed using the ACP approach, that is, “artificial systems” (A), “computational experiments,” (C) and “parallel execution” (P). Then, on the basis of the constructed parallel intelligent systems, experiments on several typical scenarios in HRSs are conducted to achieve a set of control and management strategies for actual HRSs. Experimental results show that a number of powerful tools provided by the proposed parallel intelligent systems can be utilized not only to study the current HRSs, but also to further undertake research on integrated operation control and dynamic scheduling for HRSs.

    更新日期:2018-11-16
  • Adaptive Intelligent Control for Nonlinear Strict-Feedback Systems With Virtual Control Coefficients and Uncertain Disturbances Based on Event-Triggered Mechanism
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-09-26
    Liang Cao; Hongyi Li; Qi Zhou

    This paper investigates the problem of adaptive fuzzy control on the basis of an event-triggered mechanism for nonlinear strict-feedback systems with time-varying external disturbances and virtual control coefficients in the presence of actuator failures. Virtual control coefficients are correlated with the designed adaptive law and control signal. In the backstepping technique procedure, fuzzy logic systems are utilized to approximate an unknown nonlinear function, and the tuning function is implemented to cope with the destabilizing problem of the control design. To save communication resources, an adaptive fuzzy event-triggered control strategy is developed to update the control input when the triggering condition is satisfied. Then, all of the closed-loop signals can remain semi-globally uniformly ultimately bounded. The Zeno behavior can be excluded. Finally, a numerical example and a real system are provided to illustrate the effectiveness of the proposed approach.

    更新日期:2018-11-16
  • Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount
    IEEE Trans. Cybern. (IF 8.803) 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:2018-11-16
  • Optimizing HIV Interventions for Multiplex Social Networks via Partition-Based Random Search
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-07-16
    Qingpeng Zhang; Lu Zhong; Siyang Gao; Xiaoming Li

    There are multiple modes for human immunodeficiency virus (HIV) transmissions, each of which is usually associated with a certain key population (e.g., needle sharing among people who inject drugs). Recent field studies revealed the merging trend of multiple key populations, making HIV intervention difficult because of the existence of multiple transmission modes in such complex multiplex social networks. In this paper, we aim to address this challenge by developing a multiplex social network framework to capture the multimode transmission across two key populations. Based on the multiplex social network framework, we propose a new random search method, named partition-based random search with network and memory prioritization (PRS-NMP), to identify the optimal subset of high-value individuals in the social network for interventions. Numerical experiments demonstrated that the proposed PRS-NMP-based interventions could effectively reduce the scale of HIV transmissions. The performance of PRS-NMP-based interventions is consistently better than the benchmark nested partitions method and network-based metrics.

    更新日期:2018-11-16
  • Secure Estimation for Cyber-Physical Systems via Sliding Mode
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-04-26
    Chengwei Wu; Zhongrui Hu; Jianxing Liu; Ligang Wu

    This paper is concerned with the problem of secure state reconstruction for cyber-physical systems (CPSs). CPSs are more vulnerable to the cyber world yet to attackers, who can attack any sensor of the considered systems and modify values of attacked sensors to be arbitrary ones. In the design process, both malicious attacks on sensors and unknown input are taken into consideration. First, a linear discrete-time state-space model is utilized to describe such systems, and then a sparse vector is adopted to model attacks. By collecting sensor measurements and using an iterative approach, a new model in descriptor form is obtained, which paves the way for estimating system states under an unknown input situation. Second, the problem of secure state estimation is transformed into an optimal version. A novel sliding-mode observer is proposed to estimate system states from collected sensor measurements corrupted by malicious attacks. In order to guarantee the estimations to be sparse, a projection operator is designed. Third, a projected sliding-mode observer-based estimation algorithm is developed to reconstruct system states, where an event-triggered scheme is integrated to save limited computational resource. In addition to propose such an algorithm, the effectiveness of both projection operator and sliding-mode observer is analyzed. Furthermore, the convergence of the proposed secure estimation algorithm is proved. Finally, some simulation results are given to demonstrate the effectiveness of the proposed algorithm.

    更新日期:2018-11-16
  • Reliable Control Policy of Cyber-Physical Systems Against a Class of Frequency-Constrained Sensor and Actuator Attacks
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-03-27
    Xin Huang; Jiuxiang Dong

    This paper is concerned with reliable control problems of cyber-physical systems against a class of frequency-constrained sensor and actuator attacks. We consider a continuous-time linear physical system equipped with an observer-based abnormal detector, and it is assumed that control signals and partial sensor outputs transmitted via network layers are vulnerable to cyber attacks. With the use of detection mechanisms of the abnormal monitor, an upper bound of the worst stealthy attacks is obtained, which is composed of the information of the detector’s threshold, the attack’s structure, and frequency characteristic. By exploiting the bound information, a novel attack compensator, which can stabilize the system with a nearly desired system performance, is proposed for a situation where an attack may occur without triggering an alarm. Finally, the effectiveness of the proposed control policy is verified by a numerical example.

    更新日期:2018-11-16
  • On the Parzen Kernel-Based Probability Density Function Learning Procedures Over Time-Varying Streaming Data With Applications to Pattern Classification
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-15
    Piotr Duda; Leszek Rutkowski; Maciej Jaworski; Danuta Rutkowska

    In this paper, we propose a recursive variant of the Parzen kernel density estimator (KDE) to track changes of dynamic density over data streams in a nonstationary environment. In stationary environments, well-established traditional KDE techniques have nice asymptotic properties. Their existing extensions to deal with stream data are mostly based on various heuristic concepts (losing convergence properties). In this paper, we study recursive KDEs, called recursive concept drift tracking KDEs, and prove their weak (in probability) and strong (with probability one) convergence, resulting in perfect tracking properties as the sample size approaches infinity. In three theorems and subsequent examples, we show how to choose the bandwidth and learning rate of a recursive KDE in order to ensure weak and strong convergence. The simulation results illustrate the effectiveness of our algorithm both for density estimation and classification over time-varying stream data.

    更新日期:2018-11-16
  • Observer Design of Discrete-Time Fuzzy Systems Based on an Alterable Weights Method
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-13
    Xiangpeng Xie; Dong Yue; Chen Peng

    This paper proposes an improvement on observer design of discrete-time fuzzy systems based on an alterable weights method. Different from the recent result, a more effective ranking-based switching mechanism is developed by introducing a bank of alterable weights for the sake of making use of the size difference information of the normalized fuzzy weighting functions more freely than before. Therefore, a positive result can be provided in this paper, that is, less conservative conditions of designing feasible fuzzy observers can be obtained than those existing results, while the computational cost of designing feasible fuzzy observers is even less than the up-to-date one. Finally, two numerical examples are given to show the progressiveness of the proposed method.

    更新日期:2018-11-14
  • Fast Covariance Matrix Adaptation for Large-Scale Black-Box Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-13
    Zhenhua Li; Qingfu Zhang; Xi Lin; Hui-Ling Zhen

    Covariance matrix adaptation evolution strategy (CMA-ES) is a successful gradient-free optimization algorithm. Yet, it can hardly scale to handle high-dimensional problems. In this paper, we propose a fast variant of CMA-ES (Fast CMA-ES) to handle large-scale black-box optimization problems. We approximate the covariance matrix by a low-rank matrix with a few vectors and use two of them to generate each new solution. The algorithm achieves linear internal complexity on the dimension of search space. We illustrate that the covariance matrix of the underlying distribution can be considered as an ensemble of simple models constructed by two vectors. We experimentally investigate the algorithm's behaviors and performances. It is more efficient than the CMA-ES in terms of running time. It outperforms or performs comparatively to the variant limited memory CMA-ES on large-scale problems. Finally, we evaluate the algorithm's performance with a restart strategy on the CEC'2010 large-scale global optimization benchmarks, and it shows remarkable performance and outperforms the large-scale variants of the CMA-ES.

    更新日期:2018-11-14
  • Adaptive Decentralized Controller Design for a Class of Switched Interconnected Nonlinear Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-13
    Ding Zhai; Xuan Liu; Yan-Jun Liu

    This paper is concerned with the switched decentralized adaptive control design problem for switched interconnected nonlinear systems under arbitrary switching, where the actuator failures may occur infinite times and the control directions are allowed to be unknown. By introducing a Nussbaum-type function and an integrable auxiliary signal, a switched decentralized adaptive control scheme is developed to deal with the potentially infinite times of actuator failures and the unknown control directions. The basic idea is to design different parameter update laws and control laws for distinct switched subsystems. It is proved that the state variables of the resulting closed-loop system are asymptotically stable. Finally, a numerical simulation on a double-inverted pendulum model is given to verify the proposed control scheme.

    更新日期:2018-11-14
  • Cooperative Fault Diagnosis for Uncertain Nonlinear Multiagent Systems Based on Adaptive Distributed Fuzzy Estimators
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-13
    Hong-Jun Ma; Linxing Xu

    This paper presents a cooperative fault diagnosis scheme for a class of uncertain nonlinear multiagent systems component and sensor faults in individual agents. Since the faulty system affects the healthy systems through interconnections, for each agent an estimator is designed to collect neighboring output estimations errors to consider its faulty effects on others, when computing its estimations for local state and faulty parameters. A new structure of distributed estimators is proposed by filtering regressor signals and sharing them among agents. Then, the sharings of signals are planned by properly constructing auxiliary graphs for undirected and directed networks. Two conditions are given to preselect estimators parameters for the convergences of the estimation errors. Unlike the existing results dealing with one common parameter with full state measurement and only for undirected graphs, this paper presents an output measurement-based approach for multiple parameters in undirected/directed networks. It shows that for the faults not providing persistent excitation in a signal agent, it is possible to estimate the faults exactly if the they excite all agents persistently. A simulation example of a group of single-link flexible-joint robots is given to verify the effectiveness of the proposed method.

    更新日期:2018-11-14
  • Adaptive Synchronization of Reaction-Diffusion Neural Networks and Its Application to Secure Communication
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-13
    Lakshmanan Shanmugam; Prakash Mani; Rakkiyappan Rajan; Young Hoon Joo

    This paper is mainly concerned with the synchronization problem of reaction-diffusion neural networks (RDNNs) with delays and its direct application in image secure communications. An adaptive control is designed without a sign function in which the controller gain matrix is a function of time. The synchronization criteria are established for an error model derived from master-slave models through solving the set of linear matrix inequalities derived by constructing the suitable novel Lyapunov-Krasovskii functional candidate, Green's formula, and Wirtinger's inequality. If the proposed sufficient conditions are satisfied, then the global asymptotic synchronization of the error model is guaranteed. The numerical illustrations are provided to demonstrate the validity of the derived synchronization criteria. In addition, the role of system parameters is picturized through the chaotic nature of RDNNs and those unprecedented solutions is utilized to promote better security of image transactions. As is evident, the enhancement of image encryption algorithm is designed with two levels, namely, image watermarking and diffusion process. The contributions of this paper are discussed as concluding remarks.

    更新日期:2018-11-14
  • Geometric Structural Ensemble Learning for Imbalanced Problems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-09
    Zonghai Zhu; Zhe Wang; Dongdong Li; Yujin Zhu; Wenli Du

    The classification on imbalanced data sets is a great challenge in machine learning. In this paper, a geometric structural ensemble (GSE) learning framework is proposed to address the issue. It is known that the traditional ensemble methods train and combine a series of basic classifiers according to various weights, which might lack the geometric meaning. Oppositely, the GSE partitions and eliminates redundant majority samples by generating hyper-sphere through the Euclidean metric and learns basic classifiers to enclose the minority samples, which achieves higher efficiency in the training process and seems easier to understand. In detail, the current weak classifier builds boundaries between the majority and the minority samples and removes the former. Then, the remaining samples are used to train the next. When the training process is done, all of the majority samples could be cleaned and the combination of all basic classifiers is obtained. To further improve the generalization, two relaxation techniques are proposed. Theoretically, the computational complexity of GSE could approach O(ndłog(nmin)łog(n maj)). The comprehensive experiments validate both the effectiveness and efficiency of GSE.

    更新日期:2018-11-10
  • Finite-Time Stability of Delayed Memristor-Based Fractional-Order Neural Networks
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-09
    Chongyang Chen; Song Zhu; Yongchang Wei; Chunyu Yang

    This paper studies one type of delayed memristor-based fractional-order neural networks (MFNNs) on the finite-time stability problem. By using the method of iteration, contracting mapping principle, the theory of differential inclusion, and set-valued mapping, a new criterion for the existence and uniqueness of the equilibrium point which is stable in finite time of considered MFNNs is established when the order α satisfies 0<α<1. Then, when 1<α<2, on the basis of generalized Gronwall inequality and Laplace transform, a sufficient condition ensuring the considered MFNNs stable in finite time is given. Ultimately, simulation examples are proposed to demonstrate the validity of the results.

    更新日期:2018-11-10
  • Nonparametric Estimation of Probabilistic Membership for Subspace Clustering
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-08
    Jieun Lee; Hyeogjin Lee; Minsik Lee; Nojun Kwak

    Recent advances of subspace clustering have provided a new way of constructing affinity matrices for clustering. Unlike the kernel-based subspace clustering, which needs tedious tuning among infinitely many kernel candidates, the self-expressive models derived from linear subspace assumptions in modern subspace clustering methods are rigorously combined with sparse or low-rank optimization theory to yield an affinity matrix as a solution of an optimization problem. Despite this nice theoretical aspect, the affinity matrices of modern subspace clustering have quite different meanings from the traditional ones, and even though the affinity matrices are expected to have a rough block-diagonal structure, it is unclear whether these are good enough to apply spectral clustering. In fact, most of the subspace clustering methods perform some sort of affinity value rearrangement to apply spectral clustering, but its adequacy for the spectral clustering is not clear; even though the spectral clustering step can also have a critical impact on the overall performance. To resolve this issue, in this paper, we provide a nonparametric method to estimate the probabilistic cluster membership from these affinity matrices, which we can directly find the clusters from. The likelihood for an affinity matrix is defined nonparametrically based on histograms given the probabilistic membership, which is defined as a combination of probability simplices, and an additional prior probability is defined to regularize the membership as a Bernoulli distribution. Solving this maximum a posteriori problem replaces the spectral clustering procedure, and the final discrete cluster membership can be calculated by selecting the clusters with maximum probabilities. The proposed method provides state-of-the-art performance for well-known subspace clustering methods on popular benchmark databases.

    更新日期:2018-11-09
  • Outlier Detection Using Structural Scores in a High-Dimensional Space
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-07
    Xiaojie Li; Jiancheng Lv; Zhang Yi

    Outlier detection has drawn significant interest from both academia and industry, such as network intrusion detection. Most existing methods implicitly or explicitly rely on distances in Euclidean space. However, the Euclidean distance may be incapable of measuring the similarity among high-dimensional data due to the curse of dimensionality, thus leading to inferior performance in practice. This paper presents an innovative approach for outlier detection from the view of meaningful structure scores. If two points have similar features, the difference between their structural scores is small and vice versa. The scores are calculated by measuring the variance of angles weighted by data representation, which takes the global data structure into the measurement. Thus, it could consistently rank more similar points. Compared with existing methods, our structural scores could be better to reflect the characteristics of data in a high-dimensional space. The proposed method consistently ranks more similar points. Experiments on synthetic and several real-world datasets have demonstrated the effectiveness and efficiency of our proposed methods.

    更新日期:2018-11-08
  • Weighted Hierarchical Grammatical Evolution
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-06
    Alberto Bartoli; Mauro Castelli; Eric Medvet

    Grammatical evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings, of a language defined by a user-provided context-free grammar. In this paper, we propose a novel procedure for mapping genotypes to phenotypes that we call weighted hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results of the standard GE framework as well as two of the most significant enhancements proposed in the literature: 1) position-independent GE and 2) structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.

    更新日期:2018-11-07
  • Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face Recognition
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-06
    Fei Wu; Xiao-Yuan Jing; Xiwei Dong; Ruimin Hu; Dong Yue; Lina Wang; Yi-Mu Ji; Ruchuan Wang; Guoliang Chen

    Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

    更新日期:2018-11-07
  • Multiview Semantic Representation for Visual Recognition
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-06
    Chunjie Zhang; Jian Cheng; Qi Tian

    Due to interclass and intraclass variations, the images of different classes are often cluttered which makes it hard for efficient classifications. The use of discriminative classification algorithms helps to alleviate this problem. However, it is still an open problem to accurately model the relationships between visual representations and human perception. To alleviate these problems, in this paper, we propose a novel multiview semantic representation (MVSR) algorithm for efficient visual recognition. First, we leverage visually based methods to get initial image representations. We then use both visual and semantic similarities to divide images into groups which are then used for semantic representations. We treat different image representation strategies, partition methods, and numbers as different views. A graph is then used to combine the discriminative power of different views. The similarities between images can be obtained by measuring the similarities of graphs. Finally, we train classifiers to predict the categories of images. We evaluate the discriminative power of the proposed MVSR method for visual recognition on several public image datasets. Experimental results show the effectiveness of the proposed method.

    更新日期:2018-11-07
  • Text Image Deblurring Using Kernel Sparsity Prior
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Xianyong Fang; Qiang Zhou; Jianbing Shen; Christian Jacquemin; Ling Shao

    Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel. It incorporates the L₀-norm for regularizing the blur kernel in the deblurring model, besides the L₀ sparse priors for the text image and its gradient. Such a L₀-norm-based model is efficiently optimized by half-quadratic splitting coupled with the fast conjugate descent method. To further improve the quality of the recovered kernel, a structure-preserving kernel denoising method is also developed to filter out the noisy pixels, yielding a clean kernel curve. Experimental results show the superiority of the proposed method. The source code and results are available at: https://github.com/shenjianbing/text-image-deblur.

    更新日期:2018-11-05
  • Outdoor Shadow Estimating Using Multiclass Geometric Decomposition Based on BLS
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-02
    Zhihua Chen; Ting Gao; Bin Sheng; Ping Li; C. L. Philip Chen

    Illumination is a significant component of an image, and illumination estimation of an outdoor scene from given images is still challenging yet it has wide applications. Most of the traditional illumination estimating methods require prior knowledge or fixed objects within the scene, which makes them often limited by the scene of a given image. We propose an optimization approach that integrates the multiclass cues of the image(s) [a main input image and optional auxiliary input image(s)]. First, Sun visibility is estimated by the efficient broad learning system. And then for the scene with visible Sun, we classify the information in the image by the proposed classification algorithm, which combines the geometric information and shadow information to make the most of the information. And we apply a respective algorithm for every class to estimate the illumination parameters. Finally, our approach integrates all of the estimating results by the Markov random field. We make full use of the cues in the given image instead of an extra requirement for the scene, and the qualitative results are presented and show that our approach outperformed other methods with similar conditions.

    更新日期:2018-11-05
  • Dual Encoding for Abstractive Text Summarization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-02
    Kaichun Yao; Libo Zhang; Dawei Du; Tiejian Luo; Lili Tao; Yanjun Wu

    Recurrent neural network-based sequence-to-sequence attentional models have proven effective in abstractive text summarization. In this paper, we model abstractive text summarization using a dual encoding model. Different from the previous works only using a single encoder, the proposed method employs a dual encoder including the primary and the secondary encoders. Specifically, the primary encoder conducts coarse encoding in a regular way, while the secondary encoder models the importance of words and generates more fine encoding based on the input raw text and the previously generated output text summarization. The two level encodings are combined and fed into the decoder to generate more diverse summary that can decrease repetition phenomenon for long sequence generation. The experimental results on two challenging datasets (i.e., CNN/DailyMail and DUC 2004) demonstrate that our dual encoding model performs against existing methods.

    更新日期:2018-11-05
  • hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-11-02
    Zhiang Wu; Jie Cao; Yaqiong Wang; Youquan Wang; Lu Zhang; Junjie Wu

    Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created large-scale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.

    更新日期:2018-11-05
  • Finite-Time Rigidity-Based Formation Maneuvering of Multiagent Systems Using Distributed Finite-Time Velocity Estimators
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-31
    Farhad Mehdifar; Farzad Hashemzadeh; Mahdi Baradarannia; Marcio de Queiroz

    In this paper, finite time rigidity-based formation maneuvering control of single integrator multiagent systems is considered. The target formation graph is assumed to be minimally and infinitesimally rigid, and the desired group velocity is considered to be available only to a subset of the agents. A distributed nonsmooth velocity estimator is used for each agent to estimate the desired group velocity in finite time. Using Lyapunov and input to state stability notions, a finite time distance-based formation maneuvering controller is presented and it is proved that by using the controller, agents converge to the target formation and track the desired group velocity in finite time. Furthermore, it is demonstrated that the designed controller is implementable in local coordinate frames of the agents. Simulation results are provided to show the effectiveness of the proposed control scheme.

    更新日期:2018-11-02
  • Cooperative Set Aggregation of Second-Order Multiagent Systems: Approximate Projection and Prescribed Performance
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-31
    Dandan Yue; Ziyang Meng

    This paper studies the cooperative set aggregation problem for second-order multiagent systems by utilizing the approximate projection algorithm. First, an aggregation law that uses the approximate projection is proposed, where the feasible set of the approximate projection points is established based on an Euclidean distance with respect to the targeted set and a deviated angle with respect to the exact projection point. Under the proposed law, the position vectors of all the agents are shown to reach an agreement in the intersection of their individual targeted sets and the velocity vector of each agent is shown to converge to zero. Then, a time-dependent performance bound is set for the norm of the weighted error of the relative positions among neighboring agents and the absolute velocity of the agent, and a performance-guaranteed aggregation controller is designed to guarantee the prescribed transient performance. During the process of aggregation, the norm of the weighted error is shown to not exceed the performance bound. The convergence conditions of the proposed algorithms with respect to the control strength and the terms induced by the approximate projection are obtained by using the techniques of convex analysis and Lyapunov stability. Simulations are provided to validate the theoretical results.

    更新日期:2018-11-02
  • Robust Second-Order Consensus Using a Fixed-Time Convergent Sliding Surface in Multiagent Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-30
    Jyoti P. Mishra; Chaojie Li; Mahdi Jalili; Xinghuo Yu

    Faster convergence is always sought in many applications. Designing fixed-time control has recently gained much attention since, for this type of control structure, the convergence time of the states does not depend on initial conditions, unlike other control methods providing faster convergence. This paper proposes a new distributed algorithm for second-order consensus in multiagent systems by using a full-order fixed-time convergent sliding surface. The stability analysis is performed using the Lyapunov function and bi-homogenous property. Moreover, the proposed control is smooth and free from any singularity. The robustness of the proposed scheme is verified both in the presence of Lipschitz disturbances and uncertainties in the network. The proposed method is compared with a state-of-the-art method to show the effectiveness.

    更新日期:2018-10-31
  • Neural-Network-Based Adaptive Funnel Control for Servo Mechanisms With Unknown Dead-Zone
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-30
    Shubo Wang; Haisheng Yu; Jinpeng Yu; Jing Na; Xuemei Ren

    This paper proposes an adaptive funnel control (FC) scheme for servo mechanisms with an unknown dead-zone. To improve the transient and steady-state performance, a modified funnel variable, which relaxes the limitation of the original FC (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor. Then, by applying the error transformation method, the original error is transformed into a new error variable which is used in the controller design. By using an improved funnel function in a dynamic surface control procedure, an adaptive funnel controller is proposed to guarantee that the output error remains within a predefined funnel boundary. A novel command filter technique is introduced by using the Levant differentiator to eliminate the ``explosion of complexity'' problem in the conventional backstepping procedure. Neural networks are used to approximate the unknown dead-zone and unknown nonlinear functions. Comparative experiments on a turntable servo mechanism confirm the effectiveness of the devised control method.

    更新日期:2018-10-31
  • Identification of Cellular Automata Based on Incomplete Observations With Bounded Time Gaps
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-29
    Witold Bołt; Jan M. Baetens; Bernard De Baets

    In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed.

    更新日期:2018-10-30
  • Hilbert Transform Design Based on Fractional Derivatives and Swarm Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-29
    Anil Kumar; Nikhil Agrawal; Ila Sharma; Seungchan Lee; Heung-No Lee

    This paper presents a new efficient method for implementing the Hilbert transform using an all-pass filter, based on fractional derivatives (FDs) and swarm optimization. In the proposed method, the squared error difference between the desired and designed responses of a filter is minimized. FDs are introduced to achieve higher accuracy at the reference frequency (ω ₀), which helps to reduce the overall phase error. In this paper, two approaches are used for finding the appropriate values of the FDs and reference frequencies. In the first approach, these values are estimated from a series of experiments, which require more computation time but produce less accurate results. These experiments, however, justify the behavior of the error function, with respect to the FD and ω₀, as a multimodal and nonconvex problem. In the second approach, a variant of the swarm-intelligence-based multimodal search space technique, known as the constraint-factor particle swarm optimization, is exploited for finding the suitable values for the FD and ω ₀. The performance of the proposed FD-based method is measured in terms of fidelity aspects, such as the maximum phase error, total squared phase error, maximum group delay error, and total squared group delay error. The FD-based approach is found to reduce the total phase error by 57% by exploiting only two FDs.

    更新日期:2018-10-30
  • Robust Sliding Mode-Based Learning Control for MIMO Nonlinear Nonminimum Phase System in General Form
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-26
    Xiaoxiang Hu; Changhua Hu; Xiaosheng Si; Yan Zhao

    The tracking control of a multi-input multioutput nonlinear nonminimum phase system in general form is discussed. This system is assumed to be suffering from parameter uncertainties and unmodeled dynamics, and the priori information of them is unknown. By considering both the exact model and uncertain model, the sliding mode-based learning controller is proposed. By designing an appropriate sliding surface and a learning controller, the stability of the closed-loop system is guaranteed for both the exact model and uncertain model. To overcome the disadvantage caused by parameter uncertainties and unmodeled dynamics, a fuzzy logical system is adopted here. A numerical simulation result carried on vertical takeoff and landing aircraft is taken as an example to validate the effectiveness of the presented controller.

    更新日期:2018-10-27
  • Robust Formation Control for Multiple Quadrotors With Nonlinearities and Disturbances
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-25
    Hao Liu; Teng Ma; Frank L. Lewis; Yan Wan

    In this paper, the robust formation control problem is investigated for a group of quadrotors. Each quadrotor dynamics exhibits the features of underactuation, high nonlinearities and couplings, and disturbances in both the translational and rotational motions. A distributed robust controller is developed, which consists of a position controller to govern the translational motion for the desired formation and an attitude controller to control the rotational motion of each quadrotor. Theoretical analysis and simulation studies of a formation of multiple uncertain quadrotors are presented to validate the effectiveness of the proposed formation control scheme.

    更新日期:2018-10-26
  • A Hybrid Strategy for Target Search Using Static and Mobile Sensors
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-25
    Zendai Kashino; Goldie Nejat; Beno Benhabib

    Locating a mobile target, untrackable in real-time, is pertinent to numerous time-critical applications, such as wilderness search and rescue. This paper proposes a hybrid approach to this dynamic problem, where both static and mobile sensors are utilized for the goal of detecting a target. The approach is novel in that a team of robots utilized to deploy a static-sensor network also actively searches for the target via on-board sensors. Synergy is achieved through: 1) optimal deployment planning of static-sensor networks and 2) optimal routing and motion planning of the robots for the deployment of the network and target search. The static-sensor network is planned first to maximize the likelihood of target detection while ensuring (temporal and spatial) unbiasedness in target motion. Robot motions are, subsequently, planned in two stages: 1) route planning and 2) trajectory planning. In the first stage, given a static-sensor network configuration, robot routes are planned to maximize the amount of spare time available to the mobile agents/sensors, for target search in between (just-in-time) static-sensor deployments. In the second stage, given robot routes (i.e., optimal sequences of sensor delivery locations and times), the corresponding robot trajectories are planned to make effective use of any spare time the mobile agents may have to search for the target. The proposed search strategy was validated through extensive simulations, some of which are given in detail here. An analysis of the method's performance in terms of target-search success is also included.

    更新日期:2018-10-26
  • Resilient Control of Wireless Networked Control System Under Denial-of-Service Attacks: A Cross-Layer Design Approach
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-24
    Yuan Yuan; Huanhuan Yuan; Daniel W. C. Ho; Lei Guo

    The resilient control refers to the control methodology which provides an interdisciplinary solution to secure the control system. In this paper, the resilient control problem is investigated for a class of wireless networked control systems (WNCS) under a denial-of-service (DoS) attack. In the presence of the DoS attacker, the control command sent by the transmitter may be interfered, which can cause the degradation of the signal-to-interference-plus-noise ratio and further lead to packet dropout phenomenon. Such a packet dropout phenomenon is described by a two-state Markov-chain. A cross-layer view is adopted toward the security issue of the considered WNCS. The Nash power strategies and optimal control strategy in the delta-domain are obtained in the cyber- and physical-layer, respectively. Based on the obtained strategies, the coupled-design problem is solved which aims to drive the underlying control performance to the desired security region by dynamically manipulating the cyber-layer pricing parameters. Finally, a numerical simulation is conducted to verify the validity of the proposed methodology.

    更新日期:2018-10-25
  • Consensus Tracking for Heterogeneous Interdependent Group Systems
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-23
    Huiqin Pei; Shiming Chen; Qiang Lai; Huaicheng Yan

    This paper is concerned with the consensus tracking problem for heterogeneous interdependent group systems with fixed communication topologies. First, the interdependent model of the heterogeneous system is built from the perspective of the difference of the individual characteristic and the difference of the subgroup topology structure. A class of distributed consensus tracking control protocol is proposed for realizing the consensus tracking of the heterogeneous interdependent group system via using local information. Then, for fixed communication topologies, some corresponding sufficient conditions are given to ensure the achievement of the consensus tracking. Two parameters are defined, which denote, respectively, the proportion of interdependence individual and the redundancy of interdependence. The effects of these parameters are analyzed on the consensus tracking of group systems. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.

    更新日期:2018-10-23
  • Cooperative Moving-Target Enclosing of Networked Vehicles With Constant Linear Velocities
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-23
    Xiao Yu; Ning Ding; Aidong Zhang; Huihuan Qian

    This paper investigates the cooperative moving-target enclosing control problem of networked unicycle-type nonholonomic vehicles with constant linear velocities. The information of the target is only known to some of the vehicles, and the topology of the vehicle network is described by a directed graph. A dynamic control law is proposed to steer the vehicles, such that they can get close to orbiting around the target while the target is moving with a time-vary velocity. Besides, the constraint of bounded angular velocity for the vehicles can always be satisfied. The proposed control law is distributed in the sense that each vehicle only uses its own information and the information of its neighbors in the network. Finally, simulation results of an example validate the effectiveness of the proposed control law.

    更新日期:2018-10-23
  • Adaptive Control of Nonlinear Semi-Markovian Jump T-S Fuzzy Systems With Immeasurable Premise Variables via Sliding Mode Observer
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Baoping Jiang; Hamid Reza Karimi; Yonggui Kao; Cunchen Gao

    The issue of observer-based adaptive sliding mode control of nonlinear Takagi-Sugeno fuzzy systems with semi-Markov switching and immeasurable premise variables is investigated. More general nonlinear systems are described in the model since the selections of premise variables are the states of the system. First, a novel integral sliding surface function is proposed on the observer space, then the sliding mode dynamics and error dynamics are obtained in accordance with estimated premise variables. Second, sufficient conditions for stochastic stability with an H∞ performance disturbance attenuation level ɣ of the sliding mode dynamics with different input matrices are obtained based on generally uncertain transition rates. Third, an observer-based adaptive controller is synthesized to ensure the finite time reachability of a predefined sliding surface. Finally, the single-link robot arm model is provided to verify the control scheme numerically.

    更新日期:2018-10-22
  • Generalized Conditional Domain Adaptation: A Causal Perspective With Low-Rank Translators
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 
    Chuan-Xian Ren; Xiao-Lin Xu; Hong Yan

    Learning domain adaptive features aims to enhance the classification performance of the target domain by exploring the discriminant information from an auxiliary source set. Let X denote the feature and Y as the label. The most typical problem to be addressed is that PXY has a so large variation between different domains that classification in the target domain is difficult. In this paper, we study the generalized conditional domain adaptation (DA) problem, in which both PY and PX|Y change across domains, in a causal perspective. We propose transforming the class conditional probability matching to the marginal probability matching problem, under a proper assumption. We build an intermediate domain by employing a regression model. In order to enforce the most relevant data to reconstruct the intermediate representations, a low-rank constraint is placed on the regression model for regularization. The low-rank constraint underlines a global algebraic structure between different domains, and stresses the group compactness in representing the samples. The new model is considered under the discriminant subspace framework, which is favorable in simultaneously extracting the classification information from the source domain and adaptation information across domains. The model can be solved by an alternative optimization manner of quadratic programming and the alternative Lagrange multiplier method. To the best of our knowledge, this paper is the first to exploit low-rank representation, from the source domain to the intermediate domain, to learn the domain adaptive features. Comprehensive experimental results validate that the proposed method provides better classification accuracies with DA, compared with well-established baselines.

    更新日期:2018-10-22
  • Primal Averaging: A New Gradient Evaluation Step to Attain the Optimal Individual Convergence
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-19
    Wei Tao; Zhisong Pan; Gaowei Wu; Qing Tao

    Many well-known first-order gradient methods have been extended to cope with large-scale composite problems, which often arise as a regularized empirical risk minimization in machine learning. However, their optimal convergence is attained only in terms of the weighted average of past iterative solutions. How to make the individual convergence of stochastic gradient descent (SGD) optimal, especially for strongly convex problems has now become a challenging problem in the machine learning community. On the other hand, Nesterov's recent weighted averaging strategy succeeds in achieving the optimal individual convergence of dual averaging (DA) but it fails in the basic mirror descent (MD). In this paper, a new primal averaging (PA) gradient operation step is presented, in which the gradient evaluation is imposed on the weighted average of all past iterative solutions. We prove that simply modifying the gradient operation step in MD by PA strategy suffices to recover the optimal individual rate for general convex problems. Along this line, the optimal individual rate of convergence for strongly convex problems can also be achieved by imposing the strong convexity on the gradient operation step. Furthermore, we extend PA-MD to solve regularized nonsmooth learning problems in the stochastic setting, which reveals that PA strategy is a simple yet effective extra step toward the optimal individual convergence of SGD. Several real experiments on sparse learning and SVM problems verify the correctness of our theoretical analysis.

    更新日期:2018-10-22
  • Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-19
    Mengyuan Wu; Ke Li; Sam Kwong; Qingfu Zhang

    The decomposition-based evolutionary algorithm has become an increasingly popular choice for posterior multiobjective optimization. Facing the challenges of an increasing number of objectives, many techniques have been developed which help to balance the convergence and diversity. Nevertheless, according to a recent study by Ishibuchi et al., due to the predefined search directions toward the ideal point, their performance strongly depends on the Pareto front (PF) shapes, especially the orientation of the PFs. To balance the convergence and diversity for decomposition-based methods and to alleviate their performance dependence on the orientation of the PFs, this paper develops an adversarial decomposition method for many-objective optimization, which leverages the complementary characteristics of different subproblem formulations within a single paradigm. More specifically, two populations are co-evolved by two subproblem formulations with different contours and adversarial search directions. To avoid allocating redundant computational resources to the same region of the PF, the two populations are matched into one-to-one solution pairs according to their working regions upon the PF. Each solution pair can at most contribute one principal mating parent during the mating selection process. When comparing nine state-of-the-art many-objective optimizers, we have witnessed the competitive performance of our proposed algorithm on 130 many-objective test problems with various characteristics, including regular and inverted PFs.

    更新日期:2018-10-22
  • Passivity Analysis of Delayed Neural Networks Based on Lyapunov-Krasovskii Functionals With Delay-Dependent Matrices
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-18
    Xian-Ming Zhang; Qing-Long Han; Xiaohua Ge; Bao-Lin Zhang

    This paper is concerned with passivity of a class of delayed neural networks. In order to derive less conservative passivity criteria, two Lyapunov-Krasovskii functionals (LKFs) with delay-dependent matrices are introduced by taking into consideration a second-order Bessel-Legendre inequality. In one LKF, the system state vector is coupled with those vectors inherited from the second-order Bessel-Legendre inequality through delay-dependent matrices, while no such coupling of them exists in the other LKF. These two LKFs are referred to as the coupled LKF and the noncoupled LKF, respectively. A number of delay-dependent passivity criteria are derived by employing a convex approach and a nonconvex approach to deal with the square of the time-varying delay appearing in the derivative of the LKF. Through numerical simulation, it is found that: 1) the coupled LKF is more beneficial than the noncoupled LKF for reducing the conservatism of the obtained passivity criteria and 2) the passivity criteria using the convex approach can deliver larger delay upper bounds than those using the nonconvex approach.

    更新日期:2018-10-19
  • Accelerating Convolutional Neural Networks by Removing Interspatial and Interkernel Redundancies
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-18
    Linghua Zeng; Xinmei Tian

    Recently, the high computational resource demands of convolutional neural networks (CNNs) have hindered a wide range of their applications. To solve this problem, many previous works attempted to reduce the redundant calculations during the evaluation of CNNs. However, these works mainly focused on either interspatial or interkernel redundancy. In this paper, we further accelerate existing CNNs by removing both types of redundancies. First, we convert interspatial redundancy into interkernel redundancy by decomposing one convolutional layer to one block that we design. Then, we adopt rank-selection and pruning methods to remove the interkernel redundancy. The rank-selection method, which considerably reduces manpower, contributes to determining the number of kernels to be pruned in the pruning method. We apply a layer-wise training algorithm rather than the traditional end-to-end training to overcome the difficulty of convergence. Finally, we fine-tune the entire network to achieve better performance. Our method is applied on three widely used datasets of an image classification task. We achieve better results in terms of accuracy and compression rate compared with previous state-of-the-art methods.

    更新日期:2018-10-19
  • A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-10-18
    Xinyi Le; Sijie Chen; Zheng Yan; Juntong Xi

    In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.

    更新日期:2018-10-17
  • Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-16
    Junjun Jiang; Yi Yu; Suhua Tang; Jiayi Ma; Akiko Aizawa; Kiyoharu Aizawa

    Face hallucination is a technique that reconstructs high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of the human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of the image patch. In addition, when they are confronted with misalignment or the small sample size (SSS) problem, the hallucination performance is very poor. To this end, this paper incorporates the contextual information of the image patch and proposes a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL). Under the context-patch-based framework, we advance a thresholding-based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulate the case that the HR version of the input LR face is present in the training set, it thus iteratively enhances the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. In addition, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL.

    更新日期:2018-10-17
  • An Accelerated Physarum Solver for Network Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-16
    Cai Gao; Xiaoge Zhang; Zhiying Yue; Daijun Wei

    As a novel computational paradigm, Physarum solver has received increasing attention from the researchers in tackling a plethora of network optimization problems. However, the convergence of Physarum solver is grounded by solving a system of linear equations iteratively, which often leads to low computational performance. Two factors have been highlighted along the process: 1) high time complexity in solving the system of linear equations and 2) extensive iterations required for convergence. Thus, Physarum solver has been largely restricted by its unsatisfactory computational performance. In this paper, we aim to address these two issues by developing two enhancement strategies: 1) pruning inactive nodes and 2) terminating Physarum solver in advance. First, extensive nodes and edges become and stay inactive after a few iterations in identifying the shortest path. Removing these inactive nodes and edges significantly decreases the graph size, thereby reducing computational complexity. Second, we define a transition phase for edges. All of the paths experiencing such a transition phase are dynamically aggregated to form a set of near-optimal paths among which the optimal path is included. Depth-first search is then leveraged to identify the optimal path from the near-optimal paths set. Earlier termination of Physarum solver saves considerable iterations while guaranteeing the optimality of the found solution. Empirically, 20 randomly generated sparse and complete graphs with network sizes ranging from 50 to 2000 as well as two real-world traffic networks are used to compare the performance of accelerated Physarum solver to the other two state-of-the-art algorithms.

    更新日期:2018-10-17
  • Context-Aware Semantic Inpainting
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2018-10-16
    Haofeng Li; Guanbin Li; Liang Lin; Hongchuan Yu; Yizhou Yu

    In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria.

    更新日期:2018-10-17
  • Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-09-26
    José María Luna; Mykola Pechenizkiy; María José del Jesus; Sebastián Ventura

    Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.

    更新日期:2018-10-16
  • An Adaptive Primal-Dual Subgradient Algorithm for Online Distributed Constrained Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-10-05
    Deming Yuan; Daniel W. C. Ho; Guo-Ping Jiang

    In this paper, we consider the problem of solving distributed constrained optimization over a multiagent network that consists of multiple interacting nodes in online setting, where the objective functions of nodes are time-varying and the constraint set is characterized by an inequality. Through introducing a regularized convex-concave function, we present a consensus-based adaptive primal-dual subgradient algorithm that removes the need for knowing the total number of iterations ${T}$ in advance. We show that the proposed algorithm attains an ${ \mathcal {O}(T^{1/2 + c})}$ [where ${c\in (0,1/2)}$ ] regret bound and an ${ \mathcal {O}(T^{1 - c/2})}$ bound on the violation of constraints; in addition, we show an improvement to an ${ \mathcal {O}(T^{c})}$ regret bound when the objective functions are strongly convex. The proposed algorithm allows a novel tradeoffs between the regret and the violation of constraints. Finally, a numerical example is provided to illustrate the effectiveness of the algorithm.

    更新日期:2018-10-16
  • Robust Fault Detection for Switched Fuzzy Systems With Unknown Input
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-10-03
    Jian Han; Huaguang Zhang; Yingchun Wang; Xun Sun

    This paper investigates the fault detection problem for a class of switched nonlinear systems in the T–S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted ${H_\infty }$ performance level is considered to ensure the robustness. In addition, the weighted ${H_{-}}$ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

    更新日期:2018-10-16
  • Adaptive Gradient Multiobjective Particle Swarm Optimization
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-10-09
    Honggui Han; Wei Lu; Lu Zhang; Junfei Qiao

    An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.

    更新日期:2018-10-16
  • Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework
    IEEE Trans. Cybern. (IF 8.803) Pub Date : 2017-11-01
    Yingjie Xia; Luming Zhang; Lei Meng; Yan Yan; Liqiang Nie; Xuelong Li

    To benefit the skin care, this paper aims to design an automatic and effective visual analysis framework, with the expectation of recognizing the skin disease from a given image conveying the disease affected surface. This task is nontrivial, since it is hard to collect sufficient well-labeled samples. To address such problem, we present a novel transfer learning model, which is able to incorporate external knowledge obtained from the rich and relevant Web images contributed by grassroots. In particular, we first construct a target domain by crawling a small set of images from vertical and professional dermatological websites. We then construct a source domain by collecting a large set of skin disease related images from commercial search engines. To reinforce the learning performance in the target domain, we initially build a learning model in the target domain, and then seamlessly leverage the training samples in the source domain to enhance this learning model. The distribution gap between these two domains are bridged by a linear combination of Gaussian kernels. Instead of training models with low-level features, we resort to deep models to learn the succinct, invariant, and high-level image representations. Different from previous efforts that focus on a few types of skin diseases with a small and confidential set of images generated from hospitals, this paper targets at thousands of commonly seen skin diseases with publicly accessible Web images. Hence the proposed model is easily repeatable by other researchers and extendable to other disease types. Extensive experiments on a real-world dataset have demonstrated the superiority of our proposed method over the state-of-the-art competitors.

    更新日期:2018-10-16
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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