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Eight-switch three-phase post-fault inverter (ESTPI) is widely used as the reconfigured topology of three-phase three-level neutral point clamped DC/AC inverter when open-circuit fault occurs. A key technical challenge for the ESTPI is the post-fault controller design. The existing post-fault control approaches for the ESTPI suffer from the difficulty in trading off the current tracking performance, neutral point voltage (NPV) balancing and common mode voltage (CMV) levels. To solve this problem, this paper proposes an improved predictive current control algorithm. The proposed algorithm contributes to achieve accurate output current tracking while greatly reducing the CMV with a balanced NPV. Simulation and experimental results are presented to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to refine and expand the theoretical and application framework of higher-dimensional digital chaotic system (HDDCS). Topological mixing for HDDCS is strictly proved theoretically at first. Topological mixing implies Devaney’s definition of chaos in a compact space, but not vice versa. Therefore, the proof of topological mixing promotes the theoretical research of HDDCS. Then, a general design method for constructing HDDCS via loop-state contraction algorithm is given. The construction of the iterative function uncontrolled by random sequences (hereafter called iterative function) is the starting point of this research. On this basis, this paper put forward a general design method to solve the construction problem of HDDCS, and several examples illustrate the effectiveness and feasibility of this method. The adjacency matrix corresponding to the designed HDDCS is used to construct the chaotic Echo State Network (ESN) for predicting Mackey-Glass time series. Compared with other ESNs, the chaotic ESN has better prediction performance and is able to accurately predict a much longer period of time. [ABSTRACT FROM AUTHOR]

With the development of the Internet of things (IoT), sensors are being mounted on various objects. This trend has prompted demand for low-power, high-performance information processing on the edge side. Here, an Ising model architecture that can efficiently solve optimization problems would be an efficient processing solution for edges. In this study, we implemented a 512- spin fully connected Ising model on an LSI chip fabricated in a 28-nm CMOS process. The fully connected Ising model was implemented in the chip by using pseudo-annealing (PA), which is easier to implement than simulated annealing (SA). In addition, we devised a multi-spin-thread structure, concurrent update structure, and a folded interaction placement for accuracy, speed, and compactness. Because eight spin threads are implemented, the calculation throughput could be increased by a factor of eight in comparison with a single spin-thread implementation. Moreover, as a measure of solution accuracy, the average route length of a 22-city traveling salesman problem was reduced by 19% and the standard deviation (SD) was reduced by 46%. Likewise, the average cut value of a 512- node max-cut problem was increased by 1.6% and SD was decreased by 60%. The concurrent update almost doubled the calculation speed in comparison with the case of no concurrent update. In addition, the circuit area was reduced by about 38% as a result of the folded interaction placement. The time required to obtain a solution was 128 ms. The chip at annealing processing (main processing) had a power consumption of 12 mW at 1 MHz. [ABSTRACT FROM AUTHOR]

This article concerns with terminal recurrent neural network (RNN) models for time-variant computing, featuring finite-valued activation functions (AFs), and finite-time convergence of error variables. Terminal RNNs stand for specific models that admit terminal attractors, and the dynamics of each neuron retains finite-time convergence. The might-existing imperfection in solving time-variant problems, through theoretically examining the asymptotically convergent RNNs, is pointed out for which the finite-time-convergent models are most desirable. The existing AFs are summarized, and it is found that there is a lack of the AFs that take only finite values. A finitely valued terminal RNN, among others, is taken into account, which involves only basic algebraic operations and taking roots. The proposed terminal RNN model is used to solve the time-variant problems undertaken, including the time-variant quadratic programming and motion planning of redundant manipulators. The numerical results are presented to demonstrate effectiveness of the proposed neural network, by which the convergence rate is comparable with that of the existing power-rate RNN. [ABSTRACT FROM AUTHOR]

The consensus control of multiagent systems (MASs) over randomly switching communication topologies has drawn increasing attention from researchers, since the mean square consensus or the most surely consensus is significant and practical. This manuscript focuses on the output consensus problem for heterogeneous MASs with input saturation constraints over the Markovian randomly switching topologies. The main challenge of solving the concerned problem lies in the interplay among the heterogeneous dynamics, input saturation constraints and the Markovian randomly switching topologies. To overcome such a challenge, a class of distributed adaptive observers is first designed for all agents to deal with the uncertainty caused by the randomly switching topologies. Then, a class of local state observers is presented for estimating each agent’s state information. Based on the above steps, a class of antiwindup controllers is constructed and the control gain matrices are selected using only the dynamics information of each node. Finally, the effectiveness of the consensus protocol is demonstrated. [ABSTRACT FROM AUTHOR]

This article aims to solve the problem of decentralized event-based finite-time control for a class of high-order interconnected nonlinear systems with an asymmetric output constraint. In order to achieve finite-time stability and reduce the update frequency of the controller, a novel controller and its event-trigger mechanism is proposed by adding a power integral technique and utilizing sign function. To fulfill the requirement of asymmetric output constraints, an asymmetric barrier Lyapunov function (BLF) is constructed, which is different from tan-type and log-type BLFs. Based on the contradictory idea and finite-time stability theory, it is proved that there is no Zeno phenomenon under this event-trigger mechanism and all state variables of the closed-loop system attenuate to the origin in the finite-time while the output signals never violate the constraint. Furthermore, the result in this article is expanded to fixed-time control. Finally, a simulation example is given to demonstrate the effectiveness of the presented strategy. [ABSTRACT FROM AUTHOR]

Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth inference, simply called label integration, designs proper aggregation methods to infer the unknown true label of each instance (sample) from the multiple noisy label set provided by ordinary crowd labelers (workers). However, nearly all existing label integration methods focus solely on the multiple noisy label set per individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this article. MNLDP at first estimates the multiple noisy label distribution of each instance from its multiple noisy label set and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Empirical studies on a collection of an artificial dataset, six simulated UCI datasets, and three real-world crowdsourced datasets show that MNLDP outperforms all other existing state-of-the-art label integration methods in terms of the integration accuracy and classification accuracy. [ABSTRACT FROM AUTHOR]

This article investigates the event-triggered control problem for switched affine systems (SASs). The presence of affine terms brings many difficulties on the exclusion of triggering Zeno behavior when ensuring asymptotic stability. We propose an event-triggered control strategy dynamically updated with triggering to solve this problem, in which the triggering condition is associated with the affine terms and given with a time-varying term that is updated at triggering instants. Based on the triggering strategy, the controllers for subsystems and a switching rule are designed to achieve asymptotic stability of the resulting closed-loop system. Moreover, due to the special feature of event-triggered SASs, a new method for proving the exclusion of Zeno behavior is given. Finally, the developed results are illustrated by an electric circuit example. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Recently, multisource domain adaptation (MDA) has begun to attract attention. Its performance should go beyond simply mixing all source domains together for knowledge transfer. In this article, we propose a novel prototype-based method for MDA. Specifically, for solving the problem that the target domain has no label, we use the prototype to transfer the semantic category information from source domains to target domain. First, a feature extraction network is applied to both source and target domains to obtain the extracted features from which the domain-invariant features and domain-specific features will be disentangled. Then, based on these two kinds of features, the named inherent class prototypes and domain prototypes are estimated, respectively. Then a prototype mapping to the extracted feature space is learned in the feature reconstruction process. Thus, the class prototypes for all source and target domains can be constructed in the extracted feature space based on the previous domain prototypes and inherent class prototypes. By forcing the extracted features are close to the corresponding class prototypes for all domains, the feature extraction network is progressively adjusted. In the end, the inherent class prototypes are used as a classifier in the target domain. Our contribution is that through the inherent class prototypes and domain prototypes, the semantic category information from source domains is transformed into the target domain by constructing the corresponding class prototypes. In our method, all source and target domains are aligned twice at the feature level for better domain-invariant features and more closer features to the class prototypes, respectively. Several experiments on public data sets also prove the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Hash bit selection (HBS) aims to find the most discriminative and informative hash bits from a hash pool generated by using different hashing algorithms. It is usually formulated as a binary quadratic programming problem with an information-theoretic objective function and a string-length constraint. In this article, it is equivalently reformulated in the form of a quadratic unconstrained binary optimization problem by augmenting the objective function with a penalty function. The reformulated problem is solved via collaborative neurodynamic optimization (CNO) with a population of classic discrete Hopfield networks. The two most important hyperparameters of the CNO approach are determined based on Monte Carlo test results. Experimental results on three benchmark data sets are elaborated to substantiate the superiority of the collaborative neurodynamic approach to several existing methods for HBS. [ABSTRACT FROM AUTHOR]

Taking the assumption that data samples are able to be reconstructed with the dictionary formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a consensus reconstruction matrix via exploring complementary information across multiple views. Most of them directly operate on the original data observations without preprocessing, while others operate on the corresponding kernel matrices. However, they both ignore that the collected features may be designed arbitrarily and hard guaranteed to be independent and nonoverlapping. As a result, original data observations and kernel matrices would contain a large number of redundant details. To address this issue, we propose an MSC algorithm that groups samples and removes data redundancy concurrently. In specific, eigendecomposition is employed to obtain the robust data representation of low redundancy for later clustering. By utilizing the two processes into a unified model, clustering results will guide eigendecomposition to generate more discriminative data representation, which, as feedback, helps obtain better clustering results. In addition, an alternate and convergent algorithm is designed to solve the optimization problem. Extensive experiments are conducted on eight benchmarks, and the proposed algorithm outperforms comparative ones in recent literature by a large margin, verifying its superiority. At the same time, its effectiveness, computational efficiency, and robustness to noise are validated experimentally. [ABSTRACT FROM AUTHOR]

In this article, the problem of distributed state estimation for a continuous-time linear system with a sensor network is investigated, where each sensor can only communicate with its neighbors and contains time-correlated measurement noise. To solve this problem, a novel augmented leader-following information fusion strategy is first proposed to collect measurements and system matrices. Then, a class of distributed state estimators is developed with bounded estimation error covariances. Further, a closed-form relation between the designed distributed estimator and the centralized estimator is established. It is found that the estimation performance of the former converges to that of the latter when the consensus gain tends to infinity. The proposed estimator is further extended to the fully distributed case by introducing an adaptive law for the consensus gain without using any global information. Moreover, it is shown that the designed estimator is applicable for systems with deterministic noise. Finally, several comparative numerical simulations are provided to demonstrate the effectiveness and superiority of the theoretical results. [ABSTRACT FROM AUTHOR]

This article is concerned with the global leader-following consensus problem of the input-constrained double-integrator multiagent systems. By utilizing a Lyapunov-like function consisting of a positive semidefinite term and an integral term, a class of bounded static linear protocols are proposed to ensure that the global leader-following consensus problem for homogeneous multiagent systems is solved in a fully distributed manner for all undirected communication graphs. As a further result, the global consensus problem for heterogeneous multiagent systems under directed communication graphs is also considered and two different distributed observer-based bounded linear protocols are proposed to solve such a problem also in a fully distributed manner. The most significant advantages of this article are that the system under consideration is more general, the designed protocols are linear, and the global consensus in a fully distributed manner can be guaranteed. A numerical example shows the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Recent studies in using deep learning (DL) to solve routing problems focus on construction heuristics, whose solutions are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by handcrafted rules that may limit their performance. In this article, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention-based deep architecture as the policy network to guide the selection of the next solution. We apply our method to two important routing problems, i.e., the traveling salesman problem (TSP) and the capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art DL-based approaches. The learned policies are more effective than the traditional handcrafted ones and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions, and even real-world data set. [ABSTRACT FROM AUTHOR]

In this article, we study the consensus problem in the framework of networked multiagent systems with constraint where there exists antagonistic information. A major difficulty is how to characterize the communication among the interacting agents in the presence of antagonistic information without resorting to the signed graph theory, which plays a central role in the Altafini model. It is shown that the proposed control protocol enables us to solve the consensus problem in a node-based viewpoint where both cooperative and antagonistic interactions coexist. Moreover, the proposed setup is further extended to the case of input saturation, leading to the semiglobal consensus. In addition, the consensus region associated with antagonistic information among participating individuals is also elaborated. Finally, the deduced theoretical results are applied to the task distribution problem via unmanned ground vehicles. [ABSTRACT FROM AUTHOR]

In distributed learning and optimization, a network of multiple computing units coordinates to solve a large-scale problem. This article focuses on dynamic optimization over a decentralized network. We develop a communication-efficient algorithm based on the alternating direction method of multipliers (ADMM) with quantized and censored communications, termed DQC-ADMM. At each time of the algorithm, the nodes collaborate to minimize the summation of their time-varying, local objective functions. Through local iterative computation and communication, DQC-ADMM is able to track the time-varying optimal solution. Different from traditional approaches requiring transmissions of the exact local iterates among the neighbors at every time, we propose to quantize the transmitted information, as well as adopt a communication-censoring strategy for the sake of reducing the communication cost in the optimization process. To be specific, a node transmits the quantized version of the local information to its neighbors, if and only if the value sufficiently deviates from the one previously transmitted. We theoretically justify that the proposed DQC-ADMM is capable of tracking the time-varying optimal solution, subject to a bounded error caused by the quantized and censored communications, as well as the system dynamics. Through numerical experiments, we evaluate the tracking performance and communication savings of the proposed DQC-ADMM. [ABSTRACT FROM AUTHOR]

This article proposes a distributed error estimation approach to investigate the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws under a directed communication topology. By introducing a pining control strategy, a novel class of distributed error estimation algorithms is proposed for solving the quasi-synchronization problem of heterogeneous dynamical networks with a static coupling law by exploring the local information among the neighboring nodes. By removing the assumption that the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph must be known, we also address the case of the quasi-synchronization of heterogeneous dynamical networks with nonidentical nonlinear dynamics and an adaptive coupling law. It is proved, in the sense of Lyapunov function, that the distributed error estimation for the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws will be achieved with a bounded synchronization error level. Finally, two examples are presented to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

This article studies the multiagent control problem for affine, rigid, and translational formation, with the aim of developing a unified distributed control strategy for global and finite-time convergence. Global stabilization of rigid formation in arbitrary dimensional spaces still remains open and challenging. This article provides a general solution to this open problem based on the sliding mode control idea. The control law design consists of two parts: the main control force regulates the trajectories of all agents to reach a sliding surface defined by an affine formation space in finite time and remain in it thereafter; the extra control force governs certain chosen leader agents towards the desired formation in the sliding surface. The article then presents in detail two approaches for designing the extra control force, one based on distance constraints and the other based on relative position constraints. For the first time, the proposed sliding mode formation control laws solve the open problem of (almost) global and finite-time stabilization of affine, rigid, and translational formations in any dimensional space. Simulation results are provided to illustrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

Most of the deep quantization methods adopt unsupervised approaches, and the quantization process usually occurs in the Euclidean space on top of the deep feature and its approximate value. When this approach is applied to the retrieval tasks, since the internal product space of the retrieval process is different from the Euclidean space of quantization, minimizing the quantization error (QE) does not necessarily lead to a good performance on the maximum inner product search (MIPS). To solve these problems, we treat Softmax classification as vector quantization (VQ) with angular decision boundaries and propose angular deep supervised VQ (ADSVQ) for image retrieval. Our approach can simultaneously learn the discriminative feature representation and the updatable codebook, both lying on a hypersphere. To reduce the QE between centroids and deep features, two regularization terms are proposed as supervision signals to encourage the intra-class compactness and inter-class balance, respectively. ADSVQ explicitly reformulates the asymmetric distance computation in MIPS to transform the image retrieval process into a two-stage classification process. Moreover, we discuss the extension of multiple-label cases from the perspective of quantization with binary classification. Extensive experiments demonstrate that the proposed ADSVQ has excellent performance on four well-known image data sets when compared with the state-of-the-art hashing methods. [ABSTRACT FROM AUTHOR]

A recent research direction in data-driven modeling is the identification of dynamic networks, in which measured vertex signals are interconnected by dynamic edges represented by causal linear transfer functions. The major question addressed in this article is where to allocate external excitation signals such that a network model set becomes generically identifiable when measuring all vertex signals. To tackle this synthesis problem, a novel graph structure, referred to as directed pseudotree, is introduced, and the generic identifiability of a network model set can be featured by a set of disjoint directed pseudotrees that cover all the parameterized edges of an extended graph, which includes the correlation structure of the process noises. Thereby, an algorithmic procedure is devised, aiming to decompose the extended graph into a minimal number of disjoint pseudotrees, whose roots then provide the appropriate locations for excitation signals. Furthermore, the proposed approach can be adapted using the notion of antipseudotrees to solve a dual problem, which is to select a minimal number of measurement signals for generic identifiability of the overall network, under the assumption that all the vertices are excited. [ABSTRACT FROM AUTHOR]

This article studies fixed-time stabilization (FxTS) of a general controllable linear system with an input delay $\tau$. It is shown that such a problem is not solvable if the prescribed convergence time $T_{\tau }$ is smaller than $2\tau$. For $T_{\tau }\geq 3\tau$ , a solution based on linear periodic delayed feedback (PDF) without any distributed delay is established. For $T_{\tau }>2\tau$ , a solution based on linear predictor-based PDF containing a distributed delay is proposed. For both cases, the gains of the PDF can be chosen as continuous, continuously differentiable, and even smooth, in the sense of infinitely many times differentiable. If only an output signal is available for feedback, two classes of linear observers with periodic coefficients are designed, so that their states converge to the current and future states of the system at a prescribed finite time, respectively. With the observed current and future states, FxTS can also be achieved by using, respectively, the PDF and observer-based PDF. A linear periodic feedback (without delay) is also established to solve the FxTS problem of linear systems with both instantaneous and delayed controls, which cannot be stabilized by any constant instantaneous feedback in certain cases. Two numerical examples verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

An accelerated algorithm is proposed in this article for solving the linear quadratic optimal consensus problem of multiagent systems. To optimize the linear quadratic response and the final consensus state simultaneously, a nonseparable multiobjective optimization problem with coupled constraints on decision variables is formulated. The main difficulty in solving the optimization problem lies in the nonlinear coupling of objectives, which is overcome by separating the problem into two independent and solvable single-objective optimization subproblems using the alternating direction method of multipliers. The proximal gradient decent scheme is then introduced to approximate the precise optimal solutions of the subproblems so as to improve the computing efficiency. Convergence analysis is performed to estimate the convergence rate and derive the convergence condition, which is independent of any global information of the system and, therefore, is fully distributed. Furthermore, the solution of each subproblem is obtained in a distributed form, allowing the multiagent system to achieve optimal consensus. Numerical examples show the effectiveness of the accelerated algorithm. [ABSTRACT FROM AUTHOR]

A novel approach to solve the problem of distributed state estimation of linear time-invariant systems is proposed in this article. It relies on the application of parameter-estimation-based observers, where the state observation task is reformulated as a parameter estimation problem. In contrast with existing results, our solution achieves convergence in finite time, without injection of high gain, and imposes very weak assumptions on the communication graph—namely, the existence of an open Hamiltonian walk. It is shown that this assumption is strictly weaker than the usual strong connectivity requirement. The scheme is shown to be robust vis-á-vis external disturbances and communication delays. [ABSTRACT FROM AUTHOR]

We study gradient methods for solving distributed convex optimization problems over a network when the communication bandwidth between the nodes is limited, and so information that is exchanged between the nodes must be quantized. This imperfect communication poses a fundamental challenge, and if not properly accounted for may prevent the convergence of these algorithms. Our first contribution in this article is to propose a modified consensus-based gradient method for solving such problems using random (dithered) quantization. This algorithm can be interpreted as a distributed variant of a two-time-scale stochastic approximation algorithm. We then study the convergence and derive upper bounds on the rates of convergence of the proposed method as a function of the bandwidth available between the nodes and the underlying network topology for both convex and strongly convex objective functions. Our results complement existing literature, where such convergence and explicit formulas of the convergence rates are missing. Finally, we provide numerical simulations to compare the convergence properties of the distributed gradient methods with and without quantization for solving the well-known regression problems over networks, for both quadratic and absolute loss functions. [ABSTRACT FROM AUTHOR]

In this article, a novel learning methodology is introduced for the problem of classification in the context of high-dimensional data. In particular, the challenges introduced by high-dimensional data sets are addressed by formulating a $L_{1}$ regularized zero-sum game where optimal sparsity is estimated through a two-player game between the penalty coefficients/sparsity parameters and the deep neural network weights. In order to solve this game, a distributed learning methodology is proposed where additional variables are utilized to derive layerwise cost functions. Finally, an alternating minimization approach developed to solve the problem where the Nash solution provides optimal sparsity and compensation through the classifier. The proposed learning approach is implemented in a parallel and distributed environment through a novel computational algorithm. The efficiency of the approach is demonstrated both theoretically and empirically with nine data sets. [ABSTRACT FROM AUTHOR]

We study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses), which generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging.” Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation. [ABSTRACT FROM AUTHOR]

Garbage is a global problem that each of us contributes to. Since the 1970s, we've all been told we can help fix that problem by assiduously recycling bottles and cans, boxes and newspapers. [ABSTRACT FROM AUTHOR]