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TOC Alert for Publication# 6221036 2020August 06<![CDATA[Table of contents]]>508C13423172<![CDATA[IEEE Transactions on Cybernetics]]>508C2C276<![CDATA[Adaptive Fuzzy Control With High-Order Barrier Lyapunov Functions for High-Order Uncertain Nonlinear Systems With Full-State Constraints]]>508342434321522<![CDATA[Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation]]>50834333443888<![CDATA[Interval Multiobjective Optimization With Memetic Algorithms]]>508344434579130<![CDATA[Distributed Secure Cooperative Control Under Denial-of-Service Attacks From Multiple Adversaries]]>508345834671042<![CDATA[Event-Based Secure Consensus of Mutiagent Systems Against DoS Attacks]]>50834683476911<![CDATA[A Subregion Division-Based Evolutionary Algorithm With Effective Mating Selection for Many-Objective Optimization]]>508347734902180<![CDATA[Barrier Lyapunov Function-Based Adaptive Fuzzy FTC for Switched Systems and Its Applications to Resistance–Inductance–Capacitance Circuit System]]>508349135021034<![CDATA[Scaled Consensus of Second-Order Nonlinear Multiagent Systems With Time-Varying Delays via Aperiodically Intermittent Control]]>508350335162026<![CDATA[Dual Shared-Specific Multiview Subspace Clustering]]>508351735302570<![CDATA[Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization]]>a priori knowledge, which is particularly true when the number of objectives becomes large. Depending on the shape of the Pareto front, optimal solutions such as knee points may be of interest. Although several multi- and many-objective optimization test suites have been proposed, little work has been reported focusing on designing multiobjective problems whose Pareto front contains complex knee regions. Likewise, few performance indicators dedicated to evaluate an algorithm’s ability of accurately locating all knee points in high-dimensional objective space have been suggested. This paper proposes a set of multiobjective optimization test problems whose Pareto front consists of complex knee regions, aiming to assess the capability of evolutionary algorithms to accurately identify all knee points. Various features related to knee points have been taken into account in designing the test problems, including symmetry, differentiability, and degeneration. These features are also combined with other challenges in solving the optimization problems, such as multimodality, linkage between decision variables, nonuniformity, and scalability of the Pareto front. The proposed test problems are scalable to both decision and objective spaces. Accordingly, new performance indicators are suggested for evaluating the capability of optimization algorithms in locating the knee points. The proposed test problems, together with the performance indicators, offer a new means to develop and assess preference-based evolutionary algorithms for solving multi- and many-objective optimization problems.]]>508353135444288<![CDATA[Observer-Based State Estimation of Discrete-Time Fuzzy Systems Based on a Joint Switching Mechanism for Adjacent Instants]]>50835453555825<![CDATA[Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition]]>$ell _{1}$ - or $ell _{2}$ -norm (sparsity) on the difference image itself, we introduce a weighted $ell _{2,1}$ -norm to constrain the spatial difference image cube, efficiently exploring the shared group sparse pattern. Moreover, we employ the well-known low-rank Tucker decomposition to capture the global spatial–spectral correlation from three HSI dimensions. To summarize, a weighted group sparsity-regularized low-rank tensor decomposition (LRTDGS) method is presented for HSI restoration. An efficient augmented Lagrange multiplier algorithm is employed to solve the LRTDGS model. The superiority of this method for HSI restoration is demonstrated by a series of experimental results from both simulated and real data, as compared with the other state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods.]]>508355635703170<![CDATA[Distributed Secure Filtering for Discrete-Time Systems Under Round-Robin Protocol and Deception Attacks]]>$boldsymbol {H}_{boldsymbol infty }$ performance of the closed-loop system. A corresponding filter that guarantees the security is designed. Finally, a numerical example of an inverted pendulum system is provided to demonstrate the feasibility of the proposed filter.]]>508357135801336<![CDATA[Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments]]>msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH_{2}BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.]]>508358135931392<![CDATA[Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks]]>508359436041766<![CDATA[Distributed State-Saturated Recursive Filtering Over Sensor Networks Under Round-Robin Protocol]]>508360536151226<![CDATA[Resilient Control Design Based on a Sampled-Data Model for a Class of Networked Control Systems Under Denial-of-Service Attacks]]>$N$ -order canonical Bessel–Legendre inequalities, some $N$ -dependent stability criteria are presented for the resultant closed-loop system. It is worth pointing out that a number of identity formulas are uncovered, which enable us to apply the notable free-weighting matrix approach to derive less conservative stability criteria. A linear-matrix-inequality-based criterion is provided to design stabilizing state-feedback controllers against DoS attacks. A satellite control system is given to demonstrate the effectiveness of the proposed method.]]>50836163626911<![CDATA[Going From RGB to RGBD Saliency: A Depth-Guided Transformation Model]]>508362736395265<![CDATA[Multiview Hybrid Embedding: A Divide-and-Conquer Approach]]>Divide-and-Conquer strategy, we propose multiview hybrid embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase the discriminability in intraview and interview samples, respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate the overwhelming advantages against the state-of-the-art MvSL-based cross-view classification approaches in terms of classification accuracy and robustness.]]>508364036532869<![CDATA[Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain–Computer Interfaces]]>508365436672771<![CDATA[A Survey of Optimization Methods From a Machine Learning Perspective]]>50836683681853<![CDATA[Submanifold-Preserving Discriminant Analysis With an Auto-Optimized Graph]]>${k}$ -nearest neighbors (${k}$ NNs) graph on data points. Different than previous works, our model employs the $boldsymbol {ell }_{boldsymbol {0}}$ -norm constraint and binary constraint on the similarity matrix to impose that there only be a ${k}$ nonzero value in each row of the similarity matrix, which can ensure the ${k}$ -connectivity in graph. More important, as the high-dimensional data probably contains some noises and redundant features, calculating the similarity matrix in the original space by using a kernel function is inaccurate. As a result, a mechanism of an auto-optimized graph is derived in the proposed model. Concretely, we learn the embedding space and similarity matrix simultaneously. In other words, the selection of neighbors is automatically executed in the optimal subspace rather than in the original space when the algorithm reaches convergence, which can alleviate the affect of noises and improve the robustness of the proposed model. In addition, four supervised and semisupervised local DR methods are derived by the proposed framework which can extract the discriminative fe-
tures while preserving the submanifold structure of data. Last but not least, since two variables need to be optimized simultaneously in the proposed methods, and the constraints on the similarity matrix are difficult to satisfy, which is an NP-hard problem. Consequently, an efficient iterative optimization algorithm is introduced to solve the proposed problems. Extensive experiments conducted on synthetic data and several real-world datasets have demonstrated the advantages of the proposed methods in robustness and recognition accuracy.]]>508368236952369<![CDATA[Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer]]>508369637082437<![CDATA[Control Synthesis of Hidden Semi-Markov Uncertain Fuzzy Systems via Observations of Hidden Modes]]>$mathcal {H}_{infty }$ performance of the fuzzy system. Then, the sufficient criteria for the elapsed-time-dependent and observed-mode-dependent fuzzy controller are achieved by exploiting the observations of hidden modes, ensuring that the closed-loop system is $sigma $ -error mean square stable with guaranteed $mathcal {H}_{infty }$ performance. A cart–pendulum system is used to demonstrate the effectiveness and applicability of the proposed theoretical results.]]>508370937181469<![CDATA[Neural-Network-Based Consensus Control for Multiagent Systems With Input Constraints: The Event-Triggered Case]]>50837193730733<![CDATA[Asynchronous Partially Mode-Dependent Filtering of Network-Based MSRSNSs With Quantized Measurement]]>$l_{2}-l_{infty }$ performance index are derived. Finally, an economic example is adopted to substantiate the applicability of the developed theoretical results.]]>50837313739870<![CDATA[Reference Trajectory Reshaping Optimization and Control of Robotic Exoskeletons for Human–Robot Co-Manipulation]]>508374037512261<![CDATA[Dynamic Intermittent Feedback Design for <inline-formula> <tex-math notation="LaTeX">$H_{infty}$ </tex-math></inline-formula> Containment Control on a Directed Graph]]>$H_{infty }$ containment control design is considered to attenuate the effect of the disturbance and the game algebraic Riccati equation (GARE) is employed to design the coupling and feedback gains for both static and dynamic intermittent feedback. A novel scheme is then used to unify continuous, static, and dynamic intermittent containment protocols. Finally, simulation results verify the efficacy of the proposed approach.]]>508375237652689<![CDATA[Observer-Based Fuzzy Output-Feedback Control for Discrete-Time Strict-Feedback Nonlinear Systems With Stochastic Noises]]>50837663777790<![CDATA[Type-2 Fuzzy Hybrid Controller Network for Robotic Systems]]>508377837922852<![CDATA[Robust Adaptive Fault-Tolerant Tracking Control for Nonaffine Stochastic Nonlinear Systems With Full-State Constraints]]>508379338051354<![CDATA[Synchronization of Memristive Complex-Valued Neural Networks With Time Delays via Pinning Control Method]]>50838063815615<![CDATA[Set Stabilization of Probabilistic Boolean Control Networks: A Sampled-Data Control Approach]]>50838163823273<![CDATA[Introducing IEEE Collabratec]]>508382438242056<![CDATA[IEEE Transactions on Cybernetics]]>508C3C3104<![CDATA[IEEE Transactions on Cybernetics]]>508C4C4208