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TOC Alert for Publication# 6221036 2021March 04<![CDATA[Table of contents]]>513C11098175<![CDATA[IEEE Transactions on Cybernetics]]>513C2C278<![CDATA[Reduced Adaptive Fuzzy Decoupling Control for Lower Limb Exoskeleton]]>513109911092266<![CDATA[Observer-Based Finite-Time Adaptive Fuzzy Control for Nontriangular Nonlinear Systems With Full-State Constraints]]>51311101120869<![CDATA[Compound Adaptive Fuzzy Quantized Control for Quadrotor and Its Experimental Verification]]>$ {L}_{infty }$ tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.]]>513112111333157<![CDATA[A Secure Adaptive Control for Cooperative Driving of Autonomous Connected Vehicles in the Presence of Heterogeneous Communication Delays and Cyberattacks]]>513113411492557<![CDATA[Fuzzy Adaptive Practical Fixed-Time Consensus for Second-Order Nonlinear Multiagent Systems Under Actuator Faults]]>513115011622418<![CDATA[Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm]]>513116311741130<![CDATA[Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization]]>513117511883141<![CDATA[HMM-Based Asynchronous <italic>H</italic><sub>∞</sub> Filtering for Fuzzy Singular Markovian Switching Systems With Retarded Time-Varying Delays]]>${H_{infty } }$ filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi–Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the ${H_{infty } }$ performance level. Simulation examples including a single-link robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.]]>513118912031333<![CDATA[Finite-Horizon <italic>H<sub>∞</sub></italic> State Estimation for Stochastic Coupled Networks With Random Inner Couplings Using Round-Robin Protocol]]>$H_{infty }$ state estimation for time-varying coupled stochastic networks through the round-robin scheduling protocol. The inner coupling strengths of the considered coupled networks are governed by a random sequence with known expectations and variances. For the sake of mitigating the occurrence probability of the network-induced phenomena, the communication network is equipped with the round-robin protocol that schedules the signal transmissions of the sensors’ measurement outputs. By using some dedicated approximation techniques, an uncertain auxiliary system with stochastic parameters is established where the multiplicative noises enter the coefficient matrix of the augmented disturbances. With the established auxiliary system, the desired finite-horizon $H_{infty }$ state estimator is acquired by solving coupled backward Riccati equations, and the corresponding recursive estimator design algorithm is presented that is suitable for online application. The effectiveness of the proposed estimator design method is validated via a numerical example.]]>51312041215759<![CDATA[Fault-Tolerant Attitude Control for Rigid Spacecraft Without Angular Velocity Measurements]]>513121612291144<![CDATA[Distributed Fault Estimation and Fault-Tolerant Control of Interconnected Systems]]>51312301240794<![CDATA[Distributed Edge-Based Event-Triggered Formation Control]]>513124112523451<![CDATA[Periodic Event-Triggered Suboptimal Control With Sampling Period and Performance Analysis]]>513125312611079<![CDATA[A Unified Event-Triggered Control Approach for Uncertain Pure-Feedback Systems With or Without State Constraints]]>51312621271890<![CDATA[Adaptive Controller Tuning Method Based on Online Multiobjective Optimization: A Case Study of the Four-Bar Mechanism]]>513127212851928<![CDATA[Iterative Learning Tracking for Multisensor Systems: A Weighted Optimization Approach]]>513128612991525<![CDATA[Zero-Error Consensus Tracking With Preassignable Convergence for Nonaffine Multiagent Systems]]>51313001310932<![CDATA[Reachable Set Estimation for Discrete-Time Markovian Jump Neural Networks With Generally Incomplete Transition Probabilities]]>51313111321806<![CDATA[A Novel Multiagent Neurodynamic Approach to Constrained Distributed Convex Optimization]]>513132213331750<![CDATA[PID Control for Synchronization of Complex Dynamical Networks With Directed Topologies]]>513133413461520<![CDATA[Distributed Adaptive Finite-Time Consensus for Second-Order Multiagent Systems With Mismatched Disturbances Under Directed Networks]]>513134713581642<![CDATA[Fuzzy Control Under Spatially Local Averaged Measurements for Nonlinear Distributed Parameter Systems With Time-Varying Delay]]>51313591369763<![CDATA[Event-Based Dissipative Filtering of Markovian Jump Neural Networks Subject to Incomplete Measurements and Stochastic Cyber-Attacks]]>51313701379727<![CDATA[Fuzzy Observer Constraint Based on Adaptive Control for Uncertain Nonlinear MIMO Systems With Time-Varying State Constraints]]>513138013891470<![CDATA[A Surrogate-Assisted Multiswarm Optimization Algorithm for High-Dimensional Computationally Expensive Problems]]>513139014022135<![CDATA[People-Centric Evolutionary System for Dynamic Production Scheduling]]>513140314162742<![CDATA[A Many-Objective Evolutionary Algorithm Based on a Two-Round Selection Strategy]]>513141714292344<![CDATA[A Self-Adaptive Differential Evolution Algorithm for Scheduling a Single Batch-Processing Machine With Arbitrary Job Sizes and Release Times]]>513143014422886<![CDATA[MADNet: A Fast and Lightweight Network for Single-Image Super Resolution]]>513144314531961<![CDATA[Epidemic Propagation With Positive and Negative Preventive Information in Multiplex Networks]]>513145414621404<![CDATA[GreenSea: Visual Soccer Analysis Using Broad Learning System]]>513146314772682<![CDATA[Group Reidentification with Multigrained Matching and Integration]]>513147814923388<![CDATA[Scaled Simplex Representation for Subspace Clustering]]>$s < 1$ to make it more discriminative. The proposed SSR-based SC (SSRSC) model is reformulated as a linear equality-constrained problem, which is solved efficiently under the alternating direction method of multipliers framework. Experiments on benchmark datasets demonstrate that the proposed SSRSC algorithm is very efficient and outperforms the state-of-the-art SC methods on accuracy. The code can be found at https://github.com/csjunxu/SSRSC.]]>513149315051952<![CDATA[Multimodal Learning of Social Image Representation by Exploiting Social Relations]]>513150615183175<![CDATA[Complementary Attributes: A New Clue to Zero-Shot Learning]]>513151915303999<![CDATA[A Data-Driven Aero-Engine Degradation Prognostic Strategy]]>$c$ -means algorithm, and the health state labels can be automatically assigned for health state estimation, where the uncertain initial condition and the uncertainty of health state’s transition are fully considered. Finally, a multivariate health estimation model and a multivariate multistep-ahead long-term degradation prediction model are proposed for remaining useful life estimation for aero-engines. Verification results using the aero-engine data from NASA can show that the proposed data-driven degradation prognostic strategy is effective and feasible.]]>513153115411582<![CDATA[Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review]]>513154215552030<![CDATA[SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning]]>513155615702194<![CDATA[Adaptively Weighted Multiview Proximity Learning for Clustering]]>513157115858569<![CDATA[Novel Efficient RNN and LSTM-Like Architectures: Recurrent and Gated Broad Learning Systems and Their Applications for Text Classification]]>sequence information and word importance. In this article, a kind of flat neural networks called the broad learning system (BLS) is employed to derive two novel learning methods for text classification, including recurrent BLS (R-BLS) and long short-term memory (LSTM)-like architecture: gated BLS (G-BLS). The proposed two methods possess three advantages: 1) higher accuracy due to the simultaneous learning of multiple information, even compared to deep LSTM that extracts deeper but single information only; 2) significantly faster training time due to the noniterative learning in BLS, compared to LSTM; and 3) easy integration with other discriminant information for further improvement. The proposed methods have been evaluated over 13 real-world datasets from various types of text classification. From the experimental results, the proposed methods achieve higher accuracies than LSTM while taking significantly less training time on most evaluated datasets, especially when the LSTM is in deep architecture. Compared to R-BLS, G-BLS has an extra forget gate to control the flow of information (similar to LSTM) to further improve the accuracy on text classification so that G-BLS is more effective while R-BLS is more efficient.]]>513158615971614<![CDATA[Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering]]>$k$ -means-type competitive learning. We introduce a new method called self-adaptive multiprototype-based competitive learning (SMCL) for imbalanced clusters. It uses multiple subclusters to represent each cluster with an automatic adjustment of the number of subclusters. Then, the subclusters are merged into the final clusters based on a novel separation measure. We also propose a new internal clustering validation measure to determine the number of final clusters during the merging process for imbalanced clusters. The advantages of SMCL are threefold: 1) it inherits the advantages of competitive learning and meanwhile is applicable to the imbalanced data clustering; 2) the self-adaptive multiprototype mechanism uses a proper number of subclusters to represent each cluster with any arbitrary shape; and 3) it automatically determines the number of clusters for imbalanced clusters. SMCL is compared with the existing counterparts for imbalanced clustering on the synthetic and real datasets. The experimental results show the efficacy of SMCL for imbalanced clusters.]]>513159816122801<![CDATA[Convolutional Multitimescale Echo State Network]]>513161316257353<![CDATA[A Knee-Guided Evolutionary Algorithm for Compressing Deep Neural Networks]]>compressing DNNs for accelerating its inference has drawn extensive interest recently. The basic idea is to prune parameters with little performance degradation. However, the overparameterized nature and the conflict between parameters reduction and performance maintenance make it prohibitive to manually search the pruning parameter space. In this paper, we formally establish filter pruning as a multiobjective optimization problem, and propose a knee-guided evolutionary algorithm (KGEA) that can automatically search for the solution with quality tradeoff between the scale of parameters and performance, in which both conflicting objectives can be optimized simultaneously. In particular, by incorporating a minimum Manhattan distance approach, the search effort in the proposed KGEA is explicitly guided toward the knee area, which greatly facilitates the manual search for a good tradeoff solution. Moreover, the parameter importance is directly estimated on the criterion of performance loss, which can robustly identify the redundancy. In addition to the knee solution, a performance-improved model can also be found in a fine-tuning-free fashion. The experiments on compressing fully convolutional LeNet and VGG-19 networks validate the superiority of the proposed algorithm over the state-of-the-art competing methods.]]>513162616381737<![CDATA[A Development of Granular Input Space in System Modeling]]>513163916503029<![CDATA[Large-Scale Evolution Strategy Based on Search Direction Adaptation]]>513165116655566<![CDATA[Generalized Centered 2-D Principal Component Analysis]]>$l_{2}$ -norm can mitigate the sensitivity to outliers in the domains of image analysis and pattern recognition. However, existing approaches neither preserve the structural information of data in the optimization objective nor have the robustness of generalized performance. To address the above problems, we propose two novel center-weight-based models, namely, centered PCA (C-PCA) and generalized centered 2DPCA with ${l_{2,p}}$ -norm minimization (GC-2DPCA), which are developed for vector- and matrix-based data, respectively. The C-PCA can preserve the structural information of data by measuring the similarity between the data points and can also retain the PCA’s original desirable properties such as the rotational invariance. Furthermore, GC-2DPCA can learn efficient and robust projection matrices to suppress outliers by utilizing the variations between each row of the image matrix and employing power $p$ of ${l_{2,1}}$ -norm. We also propose an efficient algorithm to solve the C-PCA model and an iterative optimization algorithm to solve the GC-2DPCA model, and we theoretically analyze their convergence properties. Experiments on three public databases show that our models yield significant improvements over the state-of-the-art PCA and 2DPCA approaches.]]>513166616771613<![CDATA[Point Set Registration With Similarity and Affine Transformations Based on Bidirectional KMPE Loss]]>${p}$ -power error (KMPE) loss, to jointly deal with the above nonideal situations. KMPE is a nonsecond-order similarity measure in kernel space and shows a strong robustness against various noise and outliers. Moreover, a bidirectional measure is applied to judge the registration, which can avoid the ill-posed problem when a lot of points converges to the same point. In particular, we develop two effective optimization methods to deal with the point set registrations with the similarity and the affine transformations, respectively. The experimental results demonstrate the effectiveness of our methods.]]>5131678168911070<![CDATA[Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multiview Learning]]>513169017031450<![CDATA[Recurrent Reconstructive Network for Sequential Anomaly Detection]]>513170417152256<![CDATA[Individuality- and Commonality-Based Multiview Multilabel Learning]]>513171617271365<![CDATA[TechRxiv: Share Your Preprint Research with the World!]]>51317281728370<![CDATA[IEEE Transactions on Cybernetics]]>513C3C3140<![CDATA[IEEE Transactions on Cybernetics]]>513C4C4246