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TOC Alert for Publication# 6221036 2021June 10<![CDATA[Table of contents]]>516C12857176<![CDATA[IEEE Transactions on Cybernetics]]>516C2C2113<![CDATA[Sliding-Mode Surface-Based Approximate Optimal Control for Uncertain Nonlinear Systems With Asymptotically Stable Critic Structure]]>516285828691259<![CDATA[Quantized Adaptive Finite-Time Bipartite NN Tracking Control for Stochastic Multiagent Systems]]>516287028811744<![CDATA[Fixed/Preassigned-Time Synchronization of Complex Networks via Improving Fixed-Time Stability]]>516288228922179<![CDATA[Output Multiformation Tracking of Networked Heterogeneous Robotic Systems via Finite-Time Hierarchical Control]]>reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.]]>516289329042904<![CDATA[Distributed Model Predictive Control for Linear–Quadratic Performance and Consensus State Optimization of Multiagent Systems]]>516290529151845<![CDATA[Distributed Secure Consensus Control With Event-Triggering for Multiagent Systems Under DoS Attacks]]>516291629282254<![CDATA[Discrete-Time Non-Zero-Sum Games With Completely Unknown Dynamics]]>$N$ -player nonzero-sum (NZS) games with completely unknown dynamics. The $N$ -coupled generalized algebraic Riccati equations (GARE) are derived, and then policy iteration (PI) algorithm is used to obtain the $N$ -tuple of iterative control and iterative value function. As the system dynamics is necessary in PI algorithm, off-policy RL method is developed for discrete-time $N$ -player NZS games. The off-policy $N$ -coupled Hamilton-Jacobi (HJ) equation is derived based on quadratic value functions. According to the Kronecker product, the $N$ -coupled HJ equation is decomposed into unknown parameter part and the system operation data part, which makes the $N$ -coupled HJ equation solved independent of system dynamics. The least square is used to calculate the iterative value function and $N$ -tuple of iterative control. The existence of Nash equilibrium is proved. The result of the proposed method for discrete-time unknown dynamics NZS games is indicated by the simulation examples.]]>51629292943839<![CDATA[Finite-Time and Fixed-Time Synchronization of Coupled Memristive Neural Networks With Time Delay]]>516294429552503<![CDATA[Fault-Tolerant Optimal Control for Discrete-Time Nonlinear System Subjected to Input Saturation: A Dynamic Event-Triggered Approach]]>516295629681157<![CDATA[Event-Triggered/Self-Triggered Leader-Following Control of Stochastic Nonlinear Multiagent Systems Using High-Gain Method]]>51629692978965<![CDATA[Event-Triggered Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Systems Preceded by Unknown Prandtl–Ishlinskii Hysteresis]]>$delta $ , and finally the $unicode {0x00A3} _{2}$ -norm transient performance of the tracking error is constructed. Simulations verify the established theoretical results that the proposed schemes successfully overcome the communication constraints and compensate the actuator PI hysteresis, and also present different tracking performances between two control schemes for comparison.]]>516297929921615<![CDATA[A Novel Path-Following-Method-Based Polynomial Fuzzy Control Design]]>51629933003849<![CDATA[A Unified Framework Design for Finite-Time and Fixed-Time Synchronization of Discontinuous Neural Networks]]>516300430162717<![CDATA[Finite-Frequency H<sup>-</sup>/H<sup>∞</sup> Fault Detection for Discrete-Time T–S Fuzzy Systems With Unmeasurable Premise Variables]]>$H_{-}/H_{infty }$ fault detection method for discrete-time T–S fuzzy systems with unmeasurable premise variables. To minimize the effect of uncertainties on system performance and maximize that of actuator faults on the generated residual, both the $H_{infty }$ disturbance attenuation index and finite-frequency $H_{-}$ fault sensitivity index are utilized. Since the premised variables are unmeasurable, the existing generalized Kalman–Yakubovich–Popov lemma cannot be directly extended to these nonlinear systems. In this paper, the conditions of allowing one to design the proposed $H_{-}/H_{infty }$ fault detection observer are established and transformed into linear matrix inequalities. Some scalars and slack matrices are introduced to bring extra degrees of freedom in observer design. Finally, a single-link robotic manipulator model is utilized to illustrate that the proposed technique can detect faults with smaller amplitude than that required by a normal $H_{infty }$ observer technique.]]>51630173026747<![CDATA[Robust Stability of Recurrent Neural Networks With Time-Varying Delays and Input Perturbation]]>$psi $ -type integral inequality, several sufficient conditions are derived for the robust stability of RNNs with discrete and distributed delays. Meanwhile, the robust boundedness of neural networks is explored by the bounded input perturbation and $mathcal {L}^{1}$ -norm constraint. Moreover, RNNs have a strong anti-jamming ability to input perturbation, and the robustness of RNNs is suitable for associative memory. Specifically, when input perturbation belongs to the specified and well-characterized space, the results cover both monostability and multistability as special cases. It is revealed that there is a relationship between the stability of neural networks and input perturbation. Compared with the existing results, these conditions proposed in this paper improve and extend the existing stability in some literature. Finally, the numerical examples are given to substantiate the effectiveness of the theoretical results.]]>516302730381029<![CDATA[Adaptive Fuzzy Tracking Control Design for a Class of Uncertain Nonstrict-Feedback Fractional-Order Nonlinear SISO Systems]]>516303930531508<![CDATA[Observer-Based Distributed Mean-Square Consensus Design for Leader-Following Multiagent Markov Jump Systems]]>516305430611423<![CDATA[Event-Triggered H<sub>∞</sub> Control for T–S Fuzzy Systems via New Asynchronous Premise Reconstruction Approach]]>$H_{infty }$ controller design for discrete-time T–S fuzzy systems under an event-triggered (ET) communication mechanism. By proposing a new asynchronous premise reconstruction approach, new types of ET fuzzy controllers are designed to overcome the challenges caused by the mismatch of premise variables, in which the gains of the designed controllers are automatically updated at different triggering instants according to an online algorithm. By constructing discontinuous Lyapunov functions, it is proved that the proposed ET controllers guarantee the stability and $H_{infty }$ performance of the closed-loop systems. Two examples are provided to verify the validity of the proposed design method.]]>51630623070845<![CDATA[Finite-Time Coverage Control for Multiagent Systems With Unidirectional Motion on a Closed Curve]]>51630713078562<![CDATA[Mayer-Type Optimal Control of Probabilistic Boolean Control Network With Uncertain Selection Probabilities]]>516307930921264<![CDATA[Event-Triggered Active MPC for Nonlinear Multiagent Systems With Packet Losses]]>51630933102831<![CDATA[Deep Reinforcement Learning for Multiobjective Optimization]]>516310331142096<![CDATA[Solving Large-Scale Multiobjective Optimization Problems With Sparse Optimal Solutions via Unsupervised Neural Networks]]>516311531282520<![CDATA[Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)]]>real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on ten benchmark problems with up to 200 decision variables. The experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.]]>516312931422407<![CDATA[Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem]]>implicitly via crossover, it intends to perform knowledge transfer explicitly among tasks in the form of task solutions, which enables the employment of task-specific search mechanisms for different optimization tasks in EMT. However, the autoencoding-based explicit EMT can only work on continuous optimization problems. It will fail on combinatorial optimization problems, which widely exist in real-world applications, such as scheduling problem, routing problem, and assignment problem. To the best of our knowledge, there is no existing effort working on explicit EMT for combinatorial optimization problems. Taking this cue, in this article, we thus embark on a study toward explicit EMT for combinatorial optimization. In particular, by using vehicle routing as an illustrative combinatorial optimization problem, the proposed explicit EMT algorithm (EEMTA) mainly contains a weighted $l_{1}$ -norm-regularized learning process for capturing the transfer mapping, and a solution-based knowledge transfer process across vehicle routing problems (VRPs). To evaluate the efficacy of the proposed EEMTA, comprehensive empirical studies have been conducted with the commonly used vehicle routing benchmarks in multitasking environment, against both the state-of-the-art EMT algorithm and the traditional single-task e-
olutionary solvers. Finally, a real-world combinatorial optimization application, that is, the package delivery problem (PDP), is also presented to further confirm the efficacy of the proposed algorithm.]]>516314331562545<![CDATA[Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model]]>516315731704532<![CDATA[Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking]]>crowdshipping and sharing economy, vehicle routing problem with occasional drivers (VRPOD) has been recently proposed to involve occasional drivers with private vehicles for the delivery of goods. In this article, we present a generalized variant of VRPOD, namely, the vehicle routing problem with heterogeneous capacity, time window, and occasional driver (VRPHTO), by taking the capacity heterogeneity and time window of vehicles into consideration. Furthermore, to meet the requirement in today’s cloud computing service, wherein multiple optimization tasks may need to be solved at the same time, we propose a novel evolutionary multitasking algorithm (EMA) to optimize multiple VRPHTOs simultaneously with a single population. Finally, 56 new VRPHTO instances are generated based on the existing common vehicle routing benchmarks. Comprehensive empirical studies are conducted to illustrate the benefits of the new VRPHTOs and to verify the efficacy of the proposed EMA for multitasking against a state-of-art single task evolutionary solver. The obtained results showed that the employment of occasional drivers could significantly reduce the routing cost, and the proposed EMA is not only able to solve multiple VRPHTOs simultaneously but also can achieve enhanced optimization performance via the knowledge transfer between tasks along the evolutionary search process.]]>516317131842844<![CDATA[Spectral–Spatial Weighted Kernel Manifold Embedded Distribution Alignment for Remote Sensing Image Classification]]>516318531973746<![CDATA[Concept Drift Detection via Equal Intensity k-Means Space Partitioning]]>$k$ -means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson’s chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify–shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.]]>516319832113373<![CDATA[Nonseparation Method-Based Finite/Fixed-Time Synchronization of Fully Complex-Valued Discontinuous Neural Networks]]>516321232231275<![CDATA[Privacy Masking Stochastic Subgradient-Push Algorithm for Distributed Online Optimization]]>51632243237727<![CDATA[An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization]]>516323832482073<![CDATA[Uniform Distribution Non-Negative Matrix Factorization for Multiview Clustering]]>516324932621725<![CDATA[Team-Triggered Practical Fixed-Time Consensus of Double-Integrator Agents With Uncertain Disturbance]]>51632633272961<![CDATA[Consensus Affinity Graph Learning for Multiple Kernel Clustering]]>$k$ neighbors sparse strategy are introduced to improve the quality of the consensus affinity graph for accurate clustering purposes. The experimental results on ten benchmark datasets and two synthetic datasets show that the proposed method consistently and significantly outperforms the state-of-the-art methods.]]>516327332842771<![CDATA[A Subvision System for Enhancing the Environmental Adaptability of the Powered Transfemoral Prosthesis]]>516328532972680<![CDATA[Correntropy-Based Multiview Subspace Clustering]]>516329833111660<![CDATA[Decentralized Observer-Based Controller Synthesis for a Large-Scale Polynomial T–S Fuzzy System With Nonlinear Interconnection Terms]]>516331233242898<![CDATA[Manifold Learning-Inspired Mating Restriction for Evolutionary Multiobjective Optimization With Complicated Pareto Sets]]>516332533372374<![CDATA[Pinning Controllability for a Boolean Network With Arbitrary Disturbance Inputs]]>51633383347319<![CDATA[A Novel Local Community Detection Method Using Evolutionary Computation]]>516334833606638<![CDATA[Distributed Containment Control for Nonlinear Stochastic Multiagent Systems]]>51633613370957<![CDATA[δ-Norm-Based Robust Regression With Applications to Image Analysis]]>$ {l_{1}}$ -norm, $ {l_{2}}$ -norm, $ {l_{2,1}}$ -norm, etc.) have been widely leveraged to form the loss function of different regression models, and have played an important role in image analysis. However, the previous regression models adopting the existing norms are sensitive to outliers and, thus, often bring about unsatisfactory results on the heavily corrupted images. This is because their adopted norms for measuring the data residual can hardly suppress the negative influence of noisy data, which will probably mislead the regression process. To address this issue, this paper proposes a novel $ {delta }$ (delta)-norm to count the nonzero blocks around an element in a vector or matrix, which weakens the impacts of outliers and also takes the structure property of examples into account. After that, we present the $ {delta }$ -norm-based robust regression (DRR) in which the data examples are mapped to the kernel space and measured by the proposed $ {delta }$ -norm. By exploring an explicit kernel function, we show that DRR has a closed-form solution, which suggests that DRR can be efficiently solved. To further handle the influences from mixed noise, DRR is extended to a multiscale version. The experimental results on image classification and background modeling datasets validate the superiority of the proposed approach to the existing state-of-the-art robust regression models.]]>516337133832856<![CDATA[QUAD-Condition, Synchronization, Consensus of Multiagents, and Anti-Synchronization of Complex Networks]]>51633843388204<![CDATA[IEEE Transactions on Cybernetics]]>516C3C3141<![CDATA[IEEE Transactions on Cybernetics]]>516C4C4282