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TOC Alert for Publication# 6221036 2019May 20<![CDATA[Table of contents]]>498C12821172<![CDATA[IEEE Transactions on Cybernetics]]>498C2C2110<![CDATA[Adaptive Fault Estimation for T–S Fuzzy Interconnected Systems Based on Persistent Excitation Condition via Reference Signals]]>498282228341051<![CDATA[Visual Servoing of Wheeled Mobile Robots Without Desired Images]]>498283528441325<![CDATA[Multilateral Teleoperation With New Cooperative Structure Based on Reconfigurable Robots and Type-2 Fuzzy Logic]]>498284528592845<![CDATA[Weighted General Group Lasso for Gene Selection in Cancer Classification]]>498286028732347<![CDATA[Data-Based Reinforcement Learning for Nonzero-Sum Games With Unknown Drift Dynamics]]>498287428851021<![CDATA[Visual Classification With Multikernel Shared Gaussian Process Latent Variable Model]]>498288628991780<![CDATA[SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection]]>498290029111934<![CDATA[Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach]]>498291229263774<![CDATA[<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-Laplacian Regularization for Scene Recognition]]>${p}$ -Laplacian, which significantly saving the computing cost. And then we propose ${p}$ -LapR (pLapR) to preserve the local geometry. Specifically, ${p}$ -Laplacian is a natural generalization of the standard graph Laplacian and provides convincing theoretical evidence to better preserve the local structure. We apply pLapR to support vector machines and kernel least squares and conduct the implementations for scene recognition. Extensive experiments on the Scene 67 dataset, Scene 15 dataset, and UC-Merced dataset validate the effectiveness of pLapR in comparison to the conventional manifold regularization methods.]]>498292729404306<![CDATA[Multiobjective Sparse Non-Negative Matrix Factorization]]>a priori knowledge is also integrated in the algorithm to reduce the computational time in discovering our interested region in the objective space. The experimental results show that the proposed paradigm has better performance than some regularization algorithms in producing solutions with different degrees of sparsity as well as high factorization accuracy, which are favorable for making the final decisions.]]>498294129542305<![CDATA[An Error Transformation Approach for Connectivity-Preserving and Collision-Avoiding Formation Tracking of Networked Uncertain Underactuated Surface Vessels]]>498295529662377<![CDATA[Finite-Time Coordination Behavior of Multiple Euler–Lagrange Systems in Cooperation-Competition Networks]]>498296729792043<![CDATA[Synchronization of Multiple Reaction–Diffusion Neural Networks With Heterogeneous and Unbounded Time-Varying Delays]]>${M}$ -matrix-based criteria are derived to justify the power-rate synchronization and exponential synchronization. In addition, new criterion on synchronization of general connected neural networks without diffusion effects is also given. Finally, two simulation examples are given to verify the effectiveness of the obtained theoretical results and provide a comparison with the existing criterion.]]>498298029911336<![CDATA[UUV’s Hierarchical DE-Based Motion Planning in a Semi Dynamic Underwater Wireless Sensor Network]]>498299230052576<![CDATA[Full Representation Data Embedding via Nonoverlapping Historical Features]]>498300630192012<![CDATA[Data-Driven Methods for Stealthy Attacks on TCP/IP-Based Networked Control Systems Equipped With Attack Detectors]]>498302030311434<![CDATA[Admissibility Analysis for Interval Type-2 Fuzzy Descriptor Systems Based on Sliding Mode Control]]>$H_{infty }$ performance. The feasibility of the proposed control method is verified by simulation results.]]>498303230401271<![CDATA[Dynamic Boundary Fuzzy Control Design of Semilinear Parabolic PDE Systems With Spatially Noncollocated Discrete Observation]]>49830413051801<![CDATA[Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning]]>498305230634259<![CDATA[Abdominal-Waving Control of Tethered Bumblebees Based on Sarsa With Transformed Reward]]>498306430731667<![CDATA[Learning Neural Representations for Network Anomaly Detection]]>498307430872121<![CDATA[Fisher Information Matrix of Unipolar Activation Function-Based Multilayer Perceptrons]]>498308830981482<![CDATA[Fixed-Time Stochastic Synchronization of Complex Networks via Continuous Control]]>49830993104527<![CDATA[Distributed Impulsive Quasi-Synchronization of Lur’e Networks With Proportional Delay]]>49831053115680<![CDATA[Robust <inline-formula> <tex-math notation="LaTeX">$H_{infty}$ </tex-math></inline-formula> Adaptive Fuzzy Tracking Control for MIMO Nonlinear Stochastic Poisson Jump Diffusion Systems]]>${{ {H}}}_{{infty }}$ tracking performance with a prescribed disturbance attenuation level. The system structure is of a strict-feedback form. Based on the backstepping design technique and ${{{H}}}_{{infty }}$ control theory, a robust adaptive control law is constructed for MIMO nonlinear stochastic Poisson jump diffusion system to achieve the ${{{H}}}_{{infty }}$ tracking performance with a prescribed attenuation level of external disturbance, despite of fuzzy approximation error and the effect of continuous and discontinuous random fluctuations. The proposed ${{ {H}}}_{{infty }}$ adaptive control law combines both merits of ${{ {H}}}_{{infty }}$ tracking control and adaptive control scheme to sufficiently solve the robust ${{ {H}}}_{{infty }}$ adaptive tracking control problem for MIMO stochastic nonlinear systems with continuous and discontinuous random fluctuations. In addition, the uniformly positive definite assumption of control coefficient matrix is relaxed for th-
proposed MIMO adaptive control as well. A stochastic quadrotor trajectory tracking control simulation is provided to show the effectiveness of the proposed ${{ {H}}}_{{infty }}$ robust adaptive control law.]]>49831163130735<![CDATA[Design of a K-Winners-Take-All Model With a Binary Spike Train]]>${K}$ -winners-take-all (KWTA) neural model that can identify the largest ${K}$ of ${N}$ inputs, where command signal $1 le {K} < {N}$ is described. The model is given by a differential equation where the spike train is a sum of delta functions. A functional block-diagram of the model includes ${N}$ feed-forward hard-limiting neurons and one feedback neuron, used to handle input dynamics. The existence and uniqueness of the model steady states are analyzed, the convergence analysis of the state variable trajectories to the KWTA operation is proven, the convergence time and number of spikes required are derived, as well as the processing of time-varying inputs and perturbations of the model nonlinearities are analyzed. The main advantage of the model is that it is not subject to the intrinsic convergence of speed limitations of comparable designs. The model also has an arbitrary finite resolution determined by a given parameter, low complexity, and initial condition independence. Applications of the model for parallel sorting and parallel rank-order filtering are presented. Theoretical results are derived and illustrated with computer-simulated examples that demonstrate the model’s performance.]]>49831313140752<![CDATA[Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns]]>498314131542263<![CDATA[Near-Optimal Resilient Control Strategy Design for State-Saturated Networked Systems Under Stochastic Communication Protocol]]>498315531671051<![CDATA[Lifelong Metric Learning]]>498316831791858<![CDATA[Fault Tolerant Nonrepetitive Trajectory Tracking for MIMO Output Constrained Nonlinear Systems Using Iterative Learning Control]]>498318031901004<![CDATA[Matching Larger Image Areas for Unconstrained Face Identification]]>block-based approach, as a complement to existing patch-based approaches, to exploit the greater discriminative information in larger areas, while maintaining robustness to limited training data. A testing block contains several neighboring patches, each of a small size. We identify the matching training block by jointly estimating all of the matching patches, as a means of reducing the uncertainty of each small matching patch with the addition of the neighboring patch information, without assuming additional training data. We further propose a multiscale extension in which we carry out block-based matching at several block sizes, to combine complementary information across scales for further robustness. We have conducted face identification experiments using three datasets, the constrained Georgia Tech dataset to validate the new approach, and two unconstrained datasets, LFW and UFI, to evaluate its potential for improving robustness. The results show that the new approach is able to significantly improve over existing patch-based face identification approaches, in the presence of uncontrolled pose, expression, and lighting variations, using small training datasets. It is also shown that the new block-based scheme can be combined with existing approaches to further improve performance.]]>498319132021138<![CDATA[Leader–Follower Consensus of Multiagent Systems With Time Delays Over Finite Fields]]>49832033208387<![CDATA[Distributed Optimization Based on a Multiagent System Disturbed by General Noise]]>49832093213510<![CDATA[IEEE Access]]>498321432141255<![CDATA[Introducing IEEE Collabratec]]>498321532152056<![CDATA[Member Get-A-Member (MGM) Program]]>498321632163457<![CDATA[IEEE Transactions on Cybernetics]]>498C3C3165<![CDATA[IEEE Transactions on Cybernetics]]>498C4C4108