<![CDATA[ IEEE Transactions on Cybernetics - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 6221036 2020June 01<![CDATA[Table of contents]]>506C12337175<![CDATA[IEEE Transactions on Cybernetics]]>506C2C2112<![CDATA[Summation Detector for False Data-Injection Attack in Cyber-Physical Systems]]>$chi ^{2}$ detector. To guarantee system security, a novel detector, that is, the summation (SUM) detector, is proposed to detect the false data-injection attacks. Different from the $chi ^{2}$ detector, the SUM detector not only utilizes the current compromise information but also collects all historical information to reveal the threat. Its evaluation value also satisfies $chi ^{2}$ distribution when no attacks compromise the systems, and the false alarm rate can be restricted to less than any given value by choosing the proper threshold value. Furthermore, an improved false data-injection attack with a time-variable increment coefficient is introduced based on the existing approaches. The effects of the SUM detector are also verified for the traditional and the improved false data-injection attacks, respectively. Finally, some simulation results are given to demonstrate the effectiveness and superiority of the SUM detector.]]>506233823451083<![CDATA[Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis]]>506234623561969<![CDATA[Unified Graph-Based Multicue Feature Fusion for Robust Visual Tracking]]>506235723689832<![CDATA[Fuzzy Output Tracking Control and Filtering for Nonlinear Discrete-Time Descriptor Systems Under Unreliable Communication Links]]>$H_{infty } $ performance for fuzzy discrete-time descriptor systems despite the uncertain Markov packet dropouts, is presented based on a fuzzy basis-dependent Lyapunov function. By resorting to the dual conditions of the obtained BRL, a solution for the designed fuzzy output tracking controller is given. A design method for the full-order fuzzy filter is also provided. Finally, two examples are finally adopted to show the applicability of the achieved design strategies.]]>50623692379979<![CDATA[Synchronization in Kuramoto Oscillator Networks With Sampled-Data Updating Law]]>50623802388806<![CDATA[Dissipativity-Based Control for Fuzzy Systems With Asynchronous Modes and Intermittent Measurements]]>506238923991394<![CDATA[Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval]]>zero-shot cross-modal retrieval, that is, the instances of the target set contain unseen classes that have inconsistent semantics with the seen classes in the source set. Inspired by zero-shot learning, we propose a novel model called ternary adversarial networks with self-supervision (TANSS) in this paper, to overcome the limitation of the existing methods on this challenging task. Our TANSS approach consists of three paralleled subnetworks: 1) two semantic feature learning subnetworks that capture the intrinsic data structures of different modalities and preserve the modality relationships via semantic features in the common semantic space; 2) a self-supervised semantic subnetwork that leverages the word vectors of both seen and unseen labels as guidance to supervise the semantic feature learning and enhances the knowledge transfer to unseen labels; and 3) we also utilize the adversarial learning scheme in our TANSS to maximize the consistency and correlation of the semantic features between different modalities. The three subnetworks are integrated in our TANSS to formulate an end-to-end network architecture which enables efficient iterative parameter optimization. Comprehensive experiments on three cross-modal datasets show the effectiveness of our TANSS approach compared with the state-of-the-art methods for zero-shot cross-modal retrieval.]]>506240024132139<![CDATA[Intermittent Discrete Observation Control for Synchronization of Stochastic Neural Networks]]>506241424241851<![CDATA[Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs]]>506242524392544<![CDATA[Exponential H∞ Filtering for Continuous-Time Switched Neural Networks Under Persistent Dwell-Time Switching Regularity]]>$ {mathcal {H}}_ {infty }$ filtering issue for a class of continuous-time switched neural networks (NNs). Our aim is to design a mode-dependent filter acquiring the state of the investigated system, and ensuring the global uniform exponential stability of the resulting filtering error system. The persistent dwell-time (PDT) switching strategy is employed to represent the switching among NNs. By utilizing a suitable Lyapunov function and the switched system theory, some criteria for the solvability of the addressed problem are presented under the full consideration of switching frequency. Finally, the filter gains are derived by a straightforward decoupling method, and with the aid of the algorithm of the continuous-time PDT switching regularity, the availability of the filter is expounded through a numerical example.]]>506244024491104<![CDATA[Hierarchical Controller-Estimator for Coordination of Networked Euler–Lagrange Systems]]>506245024612120<![CDATA[Network-Based Modeling and Proportional–Integral Control for Direct-Drive-Wheel Systems in Wireless Network Environments]]>$H_{infty }$ performance conditions with less conservatism are derived in terms of tractable linear matrix inequalities. An algorithm is presented to determine the minimum $H_{infty }$ performance and the corresponding PI control parameters by combining a particle swarm optimization technique with the performance condition. These results can be extended to a network-based PI control of general continuous-time linear systems. A ZigBee-based network simulation platform is finally built and some simulation results are provided to validate the proposed methods.]]>506246224742211<![CDATA[Learning Graph Embedding With Adversarial Training Methods]]>506247524876039<![CDATA[Flexible Linguistic Expressions and Consensus Reaching With Accurate Constraints in Group Decision-Making]]>506248825011782<![CDATA[A Consensus Community-Based Particle Swarm Optimization for Dynamic Community Detection]]>506250225133895<![CDATA[Asynchronous Distributed Algorithms for Seeking Generalized Nash Equilibria Under Full and Partial-Decision Information]]>edge Laplacian matrix, each player can carry on its iteration asynchronously with only private data and possibly delayed information from its neighbors. Then, we consider the case when agents cannot know all other players’ decisions, called a partial-decision information case. We introduce a local estimation of the overall agents’ decisions and incorporate consensus dynamics on these local estimations. The two algorithms do not need any centralized clock coordination, fully exploit the local computation resource, and remove the idle time due to waiting for the “slowest” agent. Both algorithms are developed by preconditioned forward–backward operator splitting, and their convergence is shown by relating them to asynchronous fixed-point iterations, under proper assumptions and fixed and nondiminishing step-size choices. Numerical studies verify the algorithms’ convergence and efficiency.]]>506251425261980<![CDATA[Output-Feedback Cooperative Formation Maneuvering of Autonomous Surface Vehicles With Connectivity Preservation and Collision Avoidance]]>506252725352279<![CDATA[Adaptive Neural Command Filtering Control for Nonlinear MIMO Systems With Saturation Input and Unknown Control Direction]]>506253625454408<![CDATA[Containment Control of Asynchronous Discrete-Time General Linear Multiagent Systems With Arbitrary Network Topology]]>506254625561381<![CDATA[Composite Learning Adaptive Dynamic Surface Control of Fractional-Order Nonlinear Systems]]>506255725671654<![CDATA[Finite-Time Convergence Adaptive Neural Network Control for Nonlinear Servo Systems]]>${sigma }$ -modification or ${e}$ -modification cannot guarantee the parameter estimation convergence. These nonconvergent learning methods may lead to sluggish response in the control system and make the parameter tuning complex. The aim of this paper is to propose a new learning strategy driven by the estimation error to design the alternative adaptive laws for adaptive control of nonlinear servo systems. The parameter estimation error is extracted and used as a new leakage term in the adaptive laws. By using this new learning method, the convergence of both the estimated parameters and the tracking error can be achieved simultaneously. The proposed learning algorithm is further tailored to retain finite-time convergence. To handle unknown nonlinearities in the servomechanisms, an augmented NN with a new friction model is used, where both the NN weights and some friction model coefficients are estimated online via the proposed algorithms. Comparisons with the ${sigma }$ -modification algorithm are addressed in terms of convergence property and robustness. Simulations and practical experiments are given to show the superior performance of the suggested adaptive algorithms.]]>506256825793037<![CDATA[Stability and Stabilization of T–S Fuzzy Systems With Time-Varying Delays via Delay-Product-Type Functional Method]]>50625802589641<![CDATA[Ultra-Wideband and Odometry-Based Cooperative Relative Localization With Application to Multi-UAV Formation Control]]>506259026035110<![CDATA[Dimensionality Reduction of Hyperspectral Imagery Based on Spatial–Spectral Manifold Learning]]>506260426163308<![CDATA[Finite-Time Fuzzy Control of Stochastic Nonlinear Systems]]>Lemma 5, which plays a significant role in the finite-time stability analysis of stochastic nonlinear systems. Then, the finite-time mean square stability of a stochastic nonlinear system is proved by combining Lemma 3 with Jensen’s inequality.]]>50626172626860<![CDATA[Distributed Fixed-Time Consensus Tracking Control of Uncertain Nonlinear Multiagent Systems: A Prioritized Strategy]]>506262726381418<![CDATA[Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay]]>${e}$ -modification or ${sigma }$ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov–Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.]]>506263926506667<![CDATA[New Super-Twisting Zeroing Neural-Dynamics Model for Tracking Control of Parallel Robots: A Finite-Time and Robust Solution]]>506265126601930<![CDATA[Impulsive Control of Nonlinear Systems With Time-Varying Delay and Applications]]>506266126731635<![CDATA[Temporally Identity-Aware SSD With Attentional LSTM]]>real-time online object detection in videos. In this paper, based on the attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD) for real-world detection. Distinct from the previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure, including a low-level temporal unit as well as a high-level one for multiscale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM, in which a temporal attention mechanism is specially tailored for background suppression and scale suppression, while a ConvLSTM integrates attention-aware features across time. An association loss and a multistep training are designed for temporal coherence. Besides, an online tubelet analysis (OTA) is exploited for identification. Our framework is evaluated on ImageNet VID dataset and 2DMOT15 dataset. Extensive comparisons on the detection and tracking capability validate the superiority of the proposed approach. Consequently, the developed TSSD-OTA achieves a fast speed and an overall competitive performance in terms of detection and tracking. Finally, a real-world maneuver is conducted for underwater object grasping.]]>506267426864114<![CDATA[Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control]]>${Q}$ -function over the entire large-scale traffic grid. The experimental investigations demonstrate that the proposed Coder could reduce on average 30% congestions in terms of the number of waiting vehicles during high density traffic flows in simulations.]]>506268727005662<![CDATA[Bionic Face Sketch Generator]]>506270127146629<![CDATA[Dynamic Group Learning Distributed Particle Swarm Optimization for Large-Scale Optimization and Its Application in Cloud Workflow Scheduling]]>506271527294475<![CDATA[Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment]]>506273027392061<![CDATA[Intelligent Critic Control With Robustness Guarantee of Disturbed Nonlinear Plants]]>506274027481036<![CDATA[A Hierarchical Recurrent Neural Network for Symbolic Melody Generation]]>506274927571388<![CDATA[Anti-Synchronization in Fixed Time for Discontinuous Reaction–Diffusion Neural Networks With Time-Varying Coefficients and Time Delay]]>506275827691469<![CDATA[New Criteria on Global Stabilization of Delayed Memristive Neural Networks With Inertial Item]]>506277027801959<![CDATA[High-Performance Visual Tracking With Extreme Learning Machine Framework]]>506278127924396<![CDATA[Robust Partial-Nodes-Based State Estimation for Complex Networks Under Deception Attacks]]>50627932802574<![CDATA[Torus-Event-Based Fault Diagnosis for Stochastic Multirate Time-Varying Systems With Constrained Fault]]>${H}_{infty }$ performance on the disturbance are guaranteed over the finite horizon. Especially, the residual evaluation function is employed to detect the fault, and the residual matching function is developed to isolate the fault. Furthermore, three optimization problems are provided to seek separately the minimal parameters on the ${H}_{infty }$ performance level, the upper bound of the estimation error variance, and the triggering torus. Finally, two simulation examples are utilized to show the effectiveness of the FDI scheme proposed in this paper.]]>506280328131019<![CDATA[A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization]]>506281428262504<![CDATA[Semiglobal Observer-Based Non-Negative Edge Consensus of Networked Systems With Actuator Saturation]]>50628272836927<![CDATA[Hybrid Noise-Oriented Multilabel Learning]]>506283728503730<![CDATA[Output Feedback Stabilization of Networked Control Systems Under a Stochastic Scheduling Protocol]]>50628512860793<![CDATA[Reinforcement Learning-Based Optimal Sensor Placement for Spatiotemporal Modeling]]>506286128712942<![CDATA[Transfer Clustering Ensemble Selection]]>506287228852116<![CDATA[Introducing IEEE Collabratec]]>506288628862056<![CDATA[Member Get-A-Member (MGM) Program]]>506288728873421<![CDATA[IEEE Access]]>506288828881254<![CDATA[IEEE Transactions on Cybernetics]]>506C3C3102<![CDATA[IEEE Transactions on Cybernetics]]>506C4C4174